Mammographic density assessed on paired raw and processed digital images and on paired screen-film and digital images across three mammography systems
Identifieur interne : 000B45 ( Pmc/Corpus ); précédent : 000B44; suivant : 000B46Mammographic density assessed on paired raw and processed digital images and on paired screen-film and digital images across three mammography systems
Auteurs : Anya Burton ; Graham Byrnes ; Jennifer Stone ; Rulla M. Tamimi ; John Heine ; Celine Vachon ; Vahit Ozmen ; Ana Pereira ; Maria Luisa Garmendia ; Christopher Scott ; John H. Hipwell ; Caroline Dickens ; Joachim Schüz ; Mustafa Erkin Aribal ; Kimberly Bertrand ; Ava Kwong ; Graham G. Giles ; John Hopper ; Beatriz Pérez G Mez ; Marina Pollán ; Soo-Hwang Teo ; Shivaani Mariapun ; Nur Aishah Mohd Taib ; Martín Lajous ; Ruy Lopez-Riduara ; Megan Rice ; Isabelle Romieu ; Anath Arzee Flugelman ; Giske Ursin ; Samera Qureshi ; Huiyan Ma ; Eunjung Lee ; Reza Sirous ; Mehri Sirous ; Jong Won Lee ; Jisun Kim ; Dorria Salem ; Rasha Kamal ; Mikael Hartman ; Hui Miao ; Kee-Seng Chia ; Chisato Nagata ; Sudhir Vinayak ; Rose Ndumia ; Carla H. Van Gils ; Johanna O. P. Wanders ; Beata Peplonska ; Agnieszka Bukowska ; Steve Allen ; Sarah Vinnicombe ; Sue Moss ; Anna M. Chiarelli ; Linda Linton ; Gertraud Maskarinec ; Martin J. Yaffe ; Norman F. Boyd ; Isabel Dos-Santos-Silva ; Valerie A. MccormackSource :
- Breast Cancer Research : BCR [ 1465-5411 ] ; 2016.
Abstract
Inter-women and intra-women comparisons of mammographic density (MD) are needed in research, clinical and screening applications; however, MD measurements are influenced by mammography modality (screen film/digital) and digital image format (raw/processed). We aimed to examine differences in MD assessed on these image types.
We obtained 1294 pairs of images saved in both raw and processed formats from Hologic and General Electric (GE) direct digital systems and a Fuji computed radiography (CR) system, and 128 screen-film and processed CR-digital pairs from consecutive screening rounds. Four readers performed Cumulus-based MD measurements (
Breast area did not differ in processed images compared with that in raw images, but the percent MD was higher, due to a larger dense area (median 28.5 and 25.4 cm2 respectively, mean √dense area difference 0.44 cm (95% CI: 0.36, 0.52)). This difference in √dense area was significant for direct digital systems (Hologic 0.50 cm (95% CI: 0.39, 0.61), GE 0.56 cm (95% CI: 0.42, 0.69)) but not for Fuji CR (0.06 cm (95% CI: −0.10, 0.23)). Additionally, within each system, reader-specific differences varied in magnitude and direction (
MD was slightly higher when measured on processed than on raw direct digital mammograms. Comparisons of MD on these image formats should ideally control for this non-constant and reader-specific difference.
The online version of this article (doi:10.1186/s13058-016-0787-0) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s13058-016-0787-0
PubMed: 27993168
PubMed Central: 5168805
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PMC:5168805Le document en format XML
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Tamimi</name><affiliation><nlm:aff id="Aff3">Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA</nlm:aff></affiliation></author><author><name sortKey="Heine, John" sort="Heine, John" uniqKey="Heine J" first="John" last="Heine">John Heine</name><affiliation><nlm:aff id="Aff4">Moffitt Cancer Center, Tampa, FL USA</nlm:aff></affiliation></author><author><name sortKey="Vachon, Celine" sort="Vachon, Celine" uniqKey="Vachon C" first="Celine" last="Vachon">Celine Vachon</name><affiliation><nlm:aff id="Aff5">Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA</nlm:aff></affiliation></author><author><name sortKey="Ozmen, Vahit" sort="Ozmen, Vahit" uniqKey="Ozmen V" first="Vahit" last="Ozmen">Vahit Ozmen</name><affiliation><nlm:aff id="Aff6">Department of Surgery, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey</nlm:aff></affiliation></author><author><name sortKey="Pereira, Ana" sort="Pereira, Ana" uniqKey="Pereira A" first="Ana" last="Pereira">Ana Pereira</name><affiliation><nlm:aff id="Aff7">Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile</nlm:aff></affiliation></author><author><name sortKey="Garmendia, Maria Luisa" sort="Garmendia, Maria Luisa" uniqKey="Garmendia M" first="Maria Luisa" last="Garmendia">Maria Luisa Garmendia</name><affiliation><nlm:aff id="Aff8">Centre for Medical Image Computing, University College London, London, UK</nlm:aff></affiliation></author><author><name sortKey="Scott, Christopher" sort="Scott, Christopher" uniqKey="Scott C" first="Christopher" last="Scott">Christopher Scott</name><affiliation><nlm:aff id="Aff5">Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA</nlm:aff></affiliation></author><author><name sortKey="Hipwell, John H" sort="Hipwell, John H" uniqKey="Hipwell J" first="John H." last="Hipwell">John H. Hipwell</name><affiliation><nlm:aff id="Aff8">Centre for Medical Image Computing, University College London, London, UK</nlm:aff></affiliation></author><author><name sortKey="Dickens, Caroline" sort="Dickens, Caroline" uniqKey="Dickens C" first="Caroline" last="Dickens">Caroline Dickens</name><affiliation><nlm:aff id="Aff9">Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa</nlm:aff></affiliation></author><author><name sortKey="Schuz, Joachim" sort="Schuz, Joachim" uniqKey="Schuz J" first="Joachim" last="Schüz">Joachim Schüz</name><affiliation><nlm:aff id="Aff1">Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France</nlm:aff></affiliation></author><author><name sortKey="Aribal, Mustafa Erkin" sort="Aribal, Mustafa Erkin" uniqKey="Aribal M" first="Mustafa Erkin" last="Aribal">Mustafa Erkin Aribal</name><affiliation><nlm:aff id="Aff10">Marmara University School of Medicine Department of Radiology, Istanbul, Turkey</nlm:aff></affiliation></author><author><name sortKey="Bertrand, Kimberly" sort="Bertrand, Kimberly" uniqKey="Bertrand K" first="Kimberly" last="Bertrand">Kimberly Bertrand</name><affiliation><nlm:aff id="Aff11">Slone Epidemiology Center, Boston University, Boston, MA USA</nlm:aff></affiliation></author><author><name sortKey="Kwong, Ava" sort="Kwong, Ava" uniqKey="Kwong A" first="Ava" last="Kwong">Ava Kwong</name><affiliation><nlm:aff id="Aff12">Division of Breast Surgery, Department of Surgery, The University of Hong Kong, Hong Kong, People’s Republic of China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff13">Department of Surgery, Hong Kong Sanatorium and Hospital, Hong Kong, People’s Republic of China</nlm:aff></affiliation></author><author><name sortKey="Giles, Graham G" sort="Giles, Graham G" uniqKey="Giles G" first="Graham G." last="Giles">Graham G. Giles</name><affiliation><nlm:aff id="Aff14">Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria Australia</nlm:aff></affiliation><affiliation><nlm:aff id="Aff15">Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria Australia</nlm:aff></affiliation></author><author><name sortKey="Hopper, John" sort="Hopper, John" uniqKey="Hopper J" first="John" last="Hopper">John Hopper</name><affiliation><nlm:aff id="Aff15">Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria Australia</nlm:aff></affiliation></author><author><name sortKey="Perez G Mez, Beatriz" sort="Perez G Mez, Beatriz" uniqKey="Perez G Mez B" first="Beatriz" last="Pérez G Mez">Beatriz Pérez G Mez</name><affiliation><nlm:aff id="Aff16">Cancer Epidemiology Unit, Instituto de Salud Carlos III and CIBERESP, Madrid, Spain</nlm:aff></affiliation></author><author><name sortKey="Pollan, Marina" sort="Pollan, Marina" uniqKey="Pollan M" first="Marina" last="Pollán">Marina Pollán</name><affiliation><nlm:aff id="Aff16">Cancer Epidemiology Unit, Instituto de Salud Carlos III and CIBERESP, Madrid, Spain</nlm:aff></affiliation></author><author><name sortKey="Teo, Soo Hwang" sort="Teo, Soo Hwang" uniqKey="Teo S" first="Soo-Hwang" last="Teo">Soo-Hwang Teo</name><affiliation><nlm:aff id="Aff17">Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia</nlm:aff></affiliation><affiliation><nlm:aff id="Aff18">Cancer Research Malaysia, Subang Jaya, Malaysia</nlm:aff></affiliation></author><author><name sortKey="Mariapun, Shivaani" sort="Mariapun, Shivaani" uniqKey="Mariapun S" first="Shivaani" last="Mariapun">Shivaani Mariapun</name><affiliation><nlm:aff id="Aff18">Cancer Research Malaysia, Subang Jaya, Malaysia</nlm:aff></affiliation></author><author><name sortKey="Taib, Nur Aishah Mohd" sort="Taib, Nur Aishah Mohd" uniqKey="Taib N" first="Nur Aishah Mohd" last="Taib">Nur Aishah Mohd Taib</name><affiliation><nlm:aff id="Aff17">Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia</nlm:aff></affiliation></author><author><name sortKey="Lajous, Martin" sort="Lajous, Martin" uniqKey="Lajous M" first="Martín" last="Lajous">Martín Lajous</name><affiliation><nlm:aff id="Aff19">Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA USA</nlm:aff></affiliation><affiliation><nlm:aff id="Aff20">Center for Research on Population Health, Instituto Nacional de Salud Pública, Mexico City, Mexico</nlm:aff></affiliation></author><author><name sortKey="Lopez Riduara, Ruy" sort="Lopez Riduara, Ruy" uniqKey="Lopez Riduara R" first="Ruy" last="Lopez-Riduara">Ruy Lopez-Riduara</name><affiliation><nlm:aff id="Aff20">Center for Research on Population Health, Instituto Nacional de Salud Pública, Mexico City, Mexico</nlm:aff></affiliation></author><author><name sortKey="Rice, Megan" sort="Rice, Megan" uniqKey="Rice M" first="Megan" last="Rice">Megan Rice</name><affiliation><nlm:aff id="Aff3">Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA</nlm:aff></affiliation></author><author><name sortKey="Romieu, Isabelle" sort="Romieu, Isabelle" uniqKey="Romieu I" first="Isabelle" last="Romieu">Isabelle Romieu</name><affiliation><nlm:aff id="Aff21">Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France</nlm:aff></affiliation></author><author><name sortKey="Flugelman, Anath Arzee" sort="Flugelman, Anath Arzee" uniqKey="Flugelman A" first="Anath Arzee" last="Flugelman">Anath Arzee Flugelman</name><affiliation><nlm:aff id="Aff22">National Cancer Control Center, Haifa, Israel</nlm:aff></affiliation></author><author><name sortKey="Ursin, Giske" sort="Ursin, Giske" uniqKey="Ursin G" first="Giske" last="Ursin">Giske Ursin</name><affiliation><nlm:aff id="Aff23">Cancer Registry of Norway, Oslo, Norway</nlm:aff></affiliation><affiliation><nlm:aff id="Aff24">Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway</nlm:aff></affiliation><affiliation><nlm:aff id="Aff25">Department of Preventive Medicine, University of Southern California, Los Angeles, CA USA</nlm:aff></affiliation></author><author><name sortKey="Qureshi, Samera" sort="Qureshi, Samera" uniqKey="Qureshi S" first="Samera" last="Qureshi">Samera Qureshi</name><affiliation><nlm:aff id="Aff26">Norwegian Center for Minority and Migrant Health Research (NAKMI), Oslo, Norway</nlm:aff></affiliation></author><author><name sortKey="Ma, Huiyan" sort="Ma, Huiyan" uniqKey="Ma H" first="Huiyan" last="Ma">Huiyan Ma</name><affiliation><nlm:aff id="Aff27">Department of Population Sciences, Beckman Research Institute, City of Hope, CA USA</nlm:aff></affiliation></author><author><name sortKey="Lee, Eunjung" sort="Lee, Eunjung" uniqKey="Lee E" first="Eunjung" last="Lee">Eunjung Lee</name><affiliation><nlm:aff id="Aff25">Department of Preventive Medicine, University of Southern California, Los Angeles, CA USA</nlm:aff></affiliation></author><author><name sortKey="Sirous, Reza" sort="Sirous, Reza" uniqKey="Sirous R" first="Reza" last="Sirous">Reza Sirous</name><affiliation><nlm:aff id="Aff28">Isfahan University of Medical Sciences, Isfahan, Iran</nlm:aff></affiliation></author><author><name sortKey="Sirous, Mehri" sort="Sirous, Mehri" uniqKey="Sirous M" first="Mehri" last="Sirous">Mehri Sirous</name><affiliation><nlm:aff id="Aff28">Isfahan University of Medical Sciences, Isfahan, Iran</nlm:aff></affiliation></author><author><name sortKey="Lee, Jong Won" sort="Lee, Jong Won" uniqKey="Lee J" first="Jong Won" last="Lee">Jong Won Lee</name><affiliation><nlm:aff id="Aff29">Department of Surgery, Asan Medical Center, Seoul, Republic of Korea</nlm:aff></affiliation></author><author><name sortKey="Kim, Jisun" sort="Kim, Jisun" uniqKey="Kim J" first="Jisun" last="Kim">Jisun Kim</name><affiliation><nlm:aff id="Aff29">Department of Surgery, Asan Medical Center, Seoul, Republic of Korea</nlm:aff></affiliation></author><author><name sortKey="Salem, Dorria" sort="Salem, Dorria" uniqKey="Salem D" first="Dorria" last="Salem">Dorria Salem</name><affiliation><nlm:aff id="Aff30">Cairo University, Cairo, Egypt</nlm:aff></affiliation></author><author><name sortKey="Kamal, Rasha" sort="Kamal, Rasha" uniqKey="Kamal R" first="Rasha" last="Kamal">Rasha Kamal</name><affiliation><nlm:aff id="Aff31">Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt</nlm:aff></affiliation></author><author><name sortKey="Hartman, Mikael" sort="Hartman, Mikael" uniqKey="Hartman M" first="Mikael" last="Hartman">Mikael Hartman</name><affiliation><nlm:aff id="Aff32">Department of Surgery, Yong Loo Lin School of Medicine, Singapore, Singapore</nlm:aff></affiliation><affiliation><nlm:aff id="Aff33">Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore</nlm:aff></affiliation></author><author><name sortKey="Miao, Hui" sort="Miao, Hui" uniqKey="Miao H" first="Hui" last="Miao">Hui Miao</name><affiliation><nlm:aff id="Aff33">Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore</nlm:aff></affiliation></author><author><name sortKey="Chia, Kee Seng" sort="Chia, Kee Seng" uniqKey="Chia K" first="Kee-Seng" last="Chia">Kee-Seng Chia</name><affiliation><nlm:aff id="Aff34">NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore</nlm:aff></affiliation></author><author><name sortKey="Nagata, Chisato" sort="Nagata, Chisato" uniqKey="Nagata C" first="Chisato" last="Nagata">Chisato Nagata</name><affiliation><nlm:aff id="Aff35">Gifu University, Gifu, Japan</nlm:aff></affiliation></author><author><name sortKey="Vinayak, Sudhir" sort="Vinayak, Sudhir" uniqKey="Vinayak S" first="Sudhir" last="Vinayak">Sudhir Vinayak</name><affiliation><nlm:aff id="Aff36">Aga Khan University Hospital, Nairobi, Kenya</nlm:aff></affiliation></author><author><name sortKey="Ndumia, Rose" sort="Ndumia, Rose" uniqKey="Ndumia R" first="Rose" last="Ndumia">Rose Ndumia</name><affiliation><nlm:aff id="Aff36">Aga Khan University Hospital, Nairobi, Kenya</nlm:aff></affiliation></author><author><name sortKey="Van Gils, Carla H" sort="Van Gils, Carla H" uniqKey="Van Gils C" first="Carla H." last="Van Gils">Carla H. Van Gils</name><affiliation><nlm:aff id="Aff37">Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands</nlm:aff></affiliation></author><author><name sortKey="Wanders, Johanna O P" sort="Wanders, Johanna O P" uniqKey="Wanders J" first="Johanna O. P." last="Wanders">Johanna O. P. Wanders</name><affiliation><nlm:aff id="Aff37">Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands</nlm:aff></affiliation></author><author><name sortKey="Peplonska, Beata" sort="Peplonska, Beata" uniqKey="Peplonska B" first="Beata" last="Peplonska">Beata Peplonska</name><affiliation><nlm:aff id="Aff38">Nofer Institute of Occupational Medicine, Łódz, Poland</nlm:aff></affiliation></author><author><name sortKey="Bukowska, Agnieszka" sort="Bukowska, Agnieszka" uniqKey="Bukowska A" first="Agnieszka" last="Bukowska">Agnieszka Bukowska</name><affiliation><nlm:aff id="Aff38">Nofer Institute of Occupational Medicine, Łódz, Poland</nlm:aff></affiliation></author><author><name sortKey="Allen, Steve" sort="Allen, Steve" uniqKey="Allen S" first="Steve" last="Allen">Steve Allen</name><affiliation><nlm:aff id="Aff39">Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK</nlm:aff></affiliation></author><author><name sortKey="Vinnicombe, Sarah" sort="Vinnicombe, Sarah" uniqKey="Vinnicombe S" first="Sarah" last="Vinnicombe">Sarah Vinnicombe</name><affiliation><nlm:aff id="Aff40">Division of Cancer Research, Ninewells Hospital & Medical School, Dundee, UK</nlm:aff></affiliation></author><author><name sortKey="Moss, Sue" sort="Moss, Sue" uniqKey="Moss S" first="Sue" last="Moss">Sue Moss</name><affiliation><nlm:aff id="Aff41">Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK</nlm:aff></affiliation></author><author><name sortKey="Chiarelli, Anna M" sort="Chiarelli, Anna M" uniqKey="Chiarelli A" first="Anna M." last="Chiarelli">Anna M. Chiarelli</name><affiliation><nlm:aff id="Aff42">Ontario Breast Screening Program, Cancer Care Ontario, Toronto, Canada</nlm:aff></affiliation></author><author><name sortKey="Linton, Linda" sort="Linton, Linda" uniqKey="Linton L" first="Linda" last="Linton">Linda Linton</name><affiliation><nlm:aff id="Aff43">Princess Margaret Cancer Centre, Toronto, Canada</nlm:aff></affiliation></author><author><name sortKey="Maskarinec, Gertraud" sort="Maskarinec, Gertraud" uniqKey="Maskarinec G" first="Gertraud" last="Maskarinec">Gertraud Maskarinec</name><affiliation><nlm:aff id="Aff44">University of Hawaii Cancer Center, Honolulu, HI USA</nlm:aff></affiliation></author><author><name sortKey="Yaffe, Martin J" sort="Yaffe, Martin J" uniqKey="Yaffe M" first="Martin J." last="Yaffe">Martin J. Yaffe</name><affiliation><nlm:aff id="Aff45">Medical Biophysics, University of Toronto, Toronto, Canada</nlm:aff></affiliation></author><author><name sortKey="Boyd, Norman F" sort="Boyd, Norman F" uniqKey="Boyd N" first="Norman F." last="Boyd">Norman F. Boyd</name><affiliation><nlm:aff id="Aff43">Princess Margaret Cancer Centre, Toronto, Canada</nlm:aff></affiliation></author><author><name sortKey="Dos Santos Silva, Isabel" sort="Dos Santos Silva, Isabel" uniqKey="Dos Santos Silva I" first="Isabel" last="Dos-Santos-Silva">Isabel Dos-Santos-Silva</name><affiliation><nlm:aff id="Aff46">Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK</nlm:aff></affiliation></author><author><name sortKey="Mccormack, Valerie A" sort="Mccormack, Valerie A" uniqKey="Mccormack V" first="Valerie A." last="Mccormack">Valerie A. Mccormack</name><affiliation><nlm:aff id="Aff1">Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">27993168</idno><idno type="pmc">5168805</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5168805</idno><idno type="RBID">PMC:5168805</idno><idno type="doi">10.1186/s13058-016-0787-0</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">000B45</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000B45</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Mammographic density assessed on paired raw and processed digital images and on paired screen-film and digital images across three mammography systems</title><author><name sortKey="Burton, Anya" sort="Burton, Anya" uniqKey="Burton A" first="Anya" last="Burton">Anya Burton</name><affiliation><nlm:aff id="Aff1">Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France</nlm:aff></affiliation></author><author><name sortKey="Byrnes, Graham" sort="Byrnes, Graham" uniqKey="Byrnes G" first="Graham" last="Byrnes">Graham Byrnes</name><affiliation><nlm:aff id="Aff1">Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France</nlm:aff></affiliation></author><author><name sortKey="Stone, Jennifer" sort="Stone, Jennifer" uniqKey="Stone J" first="Jennifer" last="Stone">Jennifer Stone</name><affiliation><nlm:aff id="Aff2">Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Australia</nlm:aff></affiliation></author><author><name sortKey="Tamimi, Rulla M" sort="Tamimi, Rulla M" uniqKey="Tamimi R" first="Rulla M." last="Tamimi">Rulla M. Tamimi</name><affiliation><nlm:aff id="Aff3">Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA</nlm:aff></affiliation></author><author><name sortKey="Heine, John" sort="Heine, John" uniqKey="Heine J" first="John" last="Heine">John Heine</name><affiliation><nlm:aff id="Aff4">Moffitt Cancer Center, Tampa, FL USA</nlm:aff></affiliation></author><author><name sortKey="Vachon, Celine" sort="Vachon, Celine" uniqKey="Vachon C" first="Celine" last="Vachon">Celine Vachon</name><affiliation><nlm:aff id="Aff5">Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA</nlm:aff></affiliation></author><author><name sortKey="Ozmen, Vahit" sort="Ozmen, Vahit" uniqKey="Ozmen V" first="Vahit" last="Ozmen">Vahit Ozmen</name><affiliation><nlm:aff id="Aff6">Department of Surgery, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey</nlm:aff></affiliation></author><author><name sortKey="Pereira, Ana" sort="Pereira, Ana" uniqKey="Pereira A" first="Ana" last="Pereira">Ana Pereira</name><affiliation><nlm:aff id="Aff7">Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile</nlm:aff></affiliation></author><author><name sortKey="Garmendia, Maria Luisa" sort="Garmendia, Maria Luisa" uniqKey="Garmendia M" first="Maria Luisa" last="Garmendia">Maria Luisa Garmendia</name><affiliation><nlm:aff id="Aff8">Centre for Medical Image Computing, University College London, London, UK</nlm:aff></affiliation></author><author><name sortKey="Scott, Christopher" sort="Scott, Christopher" uniqKey="Scott C" first="Christopher" last="Scott">Christopher Scott</name><affiliation><nlm:aff id="Aff5">Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA</nlm:aff></affiliation></author><author><name sortKey="Hipwell, John H" sort="Hipwell, John H" uniqKey="Hipwell J" first="John H." last="Hipwell">John H. Hipwell</name><affiliation><nlm:aff id="Aff8">Centre for Medical Image Computing, University College London, London, UK</nlm:aff></affiliation></author><author><name sortKey="Dickens, Caroline" sort="Dickens, Caroline" uniqKey="Dickens C" first="Caroline" last="Dickens">Caroline Dickens</name><affiliation><nlm:aff id="Aff9">Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa</nlm:aff></affiliation></author><author><name sortKey="Schuz, Joachim" sort="Schuz, Joachim" uniqKey="Schuz J" first="Joachim" last="Schüz">Joachim Schüz</name><affiliation><nlm:aff id="Aff1">Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France</nlm:aff></affiliation></author><author><name sortKey="Aribal, Mustafa Erkin" sort="Aribal, Mustafa Erkin" uniqKey="Aribal M" first="Mustafa Erkin" last="Aribal">Mustafa Erkin Aribal</name><affiliation><nlm:aff id="Aff10">Marmara University School of Medicine Department of Radiology, Istanbul, Turkey</nlm:aff></affiliation></author><author><name sortKey="Bertrand, Kimberly" sort="Bertrand, Kimberly" uniqKey="Bertrand K" first="Kimberly" last="Bertrand">Kimberly Bertrand</name><affiliation><nlm:aff id="Aff11">Slone Epidemiology Center, Boston University, Boston, MA USA</nlm:aff></affiliation></author><author><name sortKey="Kwong, Ava" sort="Kwong, Ava" uniqKey="Kwong A" first="Ava" last="Kwong">Ava Kwong</name><affiliation><nlm:aff id="Aff12">Division of Breast Surgery, Department of Surgery, The University of Hong Kong, Hong Kong, People’s Republic of China</nlm:aff></affiliation><affiliation><nlm:aff id="Aff13">Department of Surgery, Hong Kong Sanatorium and Hospital, Hong Kong, People’s Republic of China</nlm:aff></affiliation></author><author><name sortKey="Giles, Graham G" sort="Giles, Graham G" uniqKey="Giles G" first="Graham G." last="Giles">Graham G. Giles</name><affiliation><nlm:aff id="Aff14">Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria Australia</nlm:aff></affiliation><affiliation><nlm:aff id="Aff15">Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria Australia</nlm:aff></affiliation></author><author><name sortKey="Hopper, John" sort="Hopper, John" uniqKey="Hopper J" first="John" last="Hopper">John Hopper</name><affiliation><nlm:aff id="Aff15">Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria Australia</nlm:aff></affiliation></author><author><name sortKey="Perez G Mez, Beatriz" sort="Perez G Mez, Beatriz" uniqKey="Perez G Mez B" first="Beatriz" last="Pérez G Mez">Beatriz Pérez G Mez</name><affiliation><nlm:aff id="Aff16">Cancer Epidemiology Unit, Instituto de Salud Carlos III and CIBERESP, Madrid, Spain</nlm:aff></affiliation></author><author><name sortKey="Pollan, Marina" sort="Pollan, Marina" uniqKey="Pollan M" first="Marina" last="Pollán">Marina Pollán</name><affiliation><nlm:aff id="Aff16">Cancer Epidemiology Unit, Instituto de Salud Carlos III and CIBERESP, Madrid, Spain</nlm:aff></affiliation></author><author><name sortKey="Teo, Soo Hwang" sort="Teo, Soo Hwang" uniqKey="Teo S" first="Soo-Hwang" last="Teo">Soo-Hwang Teo</name><affiliation><nlm:aff id="Aff17">Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia</nlm:aff></affiliation><affiliation><nlm:aff id="Aff18">Cancer Research Malaysia, Subang Jaya, Malaysia</nlm:aff></affiliation></author><author><name sortKey="Mariapun, Shivaani" sort="Mariapun, Shivaani" uniqKey="Mariapun S" first="Shivaani" last="Mariapun">Shivaani Mariapun</name><affiliation><nlm:aff id="Aff18">Cancer Research Malaysia, Subang Jaya, Malaysia</nlm:aff></affiliation></author><author><name sortKey="Taib, Nur Aishah Mohd" sort="Taib, Nur Aishah Mohd" uniqKey="Taib N" first="Nur Aishah Mohd" last="Taib">Nur Aishah Mohd Taib</name><affiliation><nlm:aff id="Aff17">Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia</nlm:aff></affiliation></author><author><name sortKey="Lajous, Martin" sort="Lajous, Martin" uniqKey="Lajous M" first="Martín" last="Lajous">Martín Lajous</name><affiliation><nlm:aff id="Aff19">Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA USA</nlm:aff></affiliation><affiliation><nlm:aff id="Aff20">Center for Research on Population Health, Instituto Nacional de Salud Pública, Mexico City, Mexico</nlm:aff></affiliation></author><author><name sortKey="Lopez Riduara, Ruy" sort="Lopez Riduara, Ruy" uniqKey="Lopez Riduara R" first="Ruy" last="Lopez-Riduara">Ruy Lopez-Riduara</name><affiliation><nlm:aff id="Aff20">Center for Research on Population Health, Instituto Nacional de Salud Pública, Mexico City, Mexico</nlm:aff></affiliation></author><author><name sortKey="Rice, Megan" sort="Rice, Megan" uniqKey="Rice M" first="Megan" last="Rice">Megan Rice</name><affiliation><nlm:aff id="Aff3">Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA</nlm:aff></affiliation></author><author><name sortKey="Romieu, Isabelle" sort="Romieu, Isabelle" uniqKey="Romieu I" first="Isabelle" last="Romieu">Isabelle Romieu</name><affiliation><nlm:aff id="Aff21">Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France</nlm:aff></affiliation></author><author><name sortKey="Flugelman, Anath Arzee" sort="Flugelman, Anath Arzee" uniqKey="Flugelman A" first="Anath Arzee" last="Flugelman">Anath Arzee Flugelman</name><affiliation><nlm:aff id="Aff22">National Cancer Control Center, Haifa, Israel</nlm:aff></affiliation></author><author><name sortKey="Ursin, Giske" sort="Ursin, Giske" uniqKey="Ursin G" first="Giske" last="Ursin">Giske Ursin</name><affiliation><nlm:aff id="Aff23">Cancer Registry of Norway, Oslo, Norway</nlm:aff></affiliation><affiliation><nlm:aff id="Aff24">Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway</nlm:aff></affiliation><affiliation><nlm:aff id="Aff25">Department of Preventive Medicine, University of Southern California, Los Angeles, CA USA</nlm:aff></affiliation></author><author><name sortKey="Qureshi, Samera" sort="Qureshi, Samera" uniqKey="Qureshi S" first="Samera" last="Qureshi">Samera Qureshi</name><affiliation><nlm:aff id="Aff26">Norwegian Center for Minority and Migrant Health Research (NAKMI), Oslo, Norway</nlm:aff></affiliation></author><author><name sortKey="Ma, Huiyan" sort="Ma, Huiyan" uniqKey="Ma H" first="Huiyan" last="Ma">Huiyan Ma</name><affiliation><nlm:aff id="Aff27">Department of Population Sciences, Beckman Research Institute, City of Hope, CA USA</nlm:aff></affiliation></author><author><name sortKey="Lee, Eunjung" sort="Lee, Eunjung" uniqKey="Lee E" first="Eunjung" last="Lee">Eunjung Lee</name><affiliation><nlm:aff id="Aff25">Department of Preventive Medicine, University of Southern California, Los Angeles, CA USA</nlm:aff></affiliation></author><author><name sortKey="Sirous, Reza" sort="Sirous, Reza" uniqKey="Sirous R" first="Reza" last="Sirous">Reza Sirous</name><affiliation><nlm:aff id="Aff28">Isfahan University of Medical Sciences, Isfahan, Iran</nlm:aff></affiliation></author><author><name sortKey="Sirous, Mehri" sort="Sirous, Mehri" uniqKey="Sirous M" first="Mehri" last="Sirous">Mehri Sirous</name><affiliation><nlm:aff id="Aff28">Isfahan University of Medical Sciences, Isfahan, Iran</nlm:aff></affiliation></author><author><name sortKey="Lee, Jong Won" sort="Lee, Jong Won" uniqKey="Lee J" first="Jong Won" last="Lee">Jong Won Lee</name><affiliation><nlm:aff id="Aff29">Department of Surgery, Asan Medical Center, Seoul, Republic of Korea</nlm:aff></affiliation></author><author><name sortKey="Kim, Jisun" sort="Kim, Jisun" uniqKey="Kim J" first="Jisun" last="Kim">Jisun Kim</name><affiliation><nlm:aff id="Aff29">Department of Surgery, Asan Medical Center, Seoul, Republic of Korea</nlm:aff></affiliation></author><author><name sortKey="Salem, Dorria" sort="Salem, Dorria" uniqKey="Salem D" first="Dorria" last="Salem">Dorria Salem</name><affiliation><nlm:aff id="Aff30">Cairo University, Cairo, Egypt</nlm:aff></affiliation></author><author><name sortKey="Kamal, Rasha" sort="Kamal, Rasha" uniqKey="Kamal R" first="Rasha" last="Kamal">Rasha Kamal</name><affiliation><nlm:aff id="Aff31">Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt</nlm:aff></affiliation></author><author><name sortKey="Hartman, Mikael" sort="Hartman, Mikael" uniqKey="Hartman M" first="Mikael" last="Hartman">Mikael Hartman</name><affiliation><nlm:aff id="Aff32">Department of Surgery, Yong Loo Lin School of Medicine, Singapore, Singapore</nlm:aff></affiliation><affiliation><nlm:aff id="Aff33">Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore</nlm:aff></affiliation></author><author><name sortKey="Miao, Hui" sort="Miao, Hui" uniqKey="Miao H" first="Hui" last="Miao">Hui Miao</name><affiliation><nlm:aff id="Aff33">Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore</nlm:aff></affiliation></author><author><name sortKey="Chia, Kee Seng" sort="Chia, Kee Seng" uniqKey="Chia K" first="Kee-Seng" last="Chia">Kee-Seng Chia</name><affiliation><nlm:aff id="Aff34">NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore</nlm:aff></affiliation></author><author><name sortKey="Nagata, Chisato" sort="Nagata, Chisato" uniqKey="Nagata C" first="Chisato" last="Nagata">Chisato Nagata</name><affiliation><nlm:aff id="Aff35">Gifu University, Gifu, Japan</nlm:aff></affiliation></author><author><name sortKey="Vinayak, Sudhir" sort="Vinayak, Sudhir" uniqKey="Vinayak S" first="Sudhir" last="Vinayak">Sudhir Vinayak</name><affiliation><nlm:aff id="Aff36">Aga Khan University Hospital, Nairobi, Kenya</nlm:aff></affiliation></author><author><name sortKey="Ndumia, Rose" sort="Ndumia, Rose" uniqKey="Ndumia R" first="Rose" last="Ndumia">Rose Ndumia</name><affiliation><nlm:aff id="Aff36">Aga Khan University Hospital, Nairobi, Kenya</nlm:aff></affiliation></author><author><name sortKey="Van Gils, Carla H" sort="Van Gils, Carla H" uniqKey="Van Gils C" first="Carla H." last="Van Gils">Carla H. Van Gils</name><affiliation><nlm:aff id="Aff37">Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands</nlm:aff></affiliation></author><author><name sortKey="Wanders, Johanna O P" sort="Wanders, Johanna O P" uniqKey="Wanders J" first="Johanna O. P." last="Wanders">Johanna O. P. Wanders</name><affiliation><nlm:aff id="Aff37">Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands</nlm:aff></affiliation></author><author><name sortKey="Peplonska, Beata" sort="Peplonska, Beata" uniqKey="Peplonska B" first="Beata" last="Peplonska">Beata Peplonska</name><affiliation><nlm:aff id="Aff38">Nofer Institute of Occupational Medicine, Łódz, Poland</nlm:aff></affiliation></author><author><name sortKey="Bukowska, Agnieszka" sort="Bukowska, Agnieszka" uniqKey="Bukowska A" first="Agnieszka" last="Bukowska">Agnieszka Bukowska</name><affiliation><nlm:aff id="Aff38">Nofer Institute of Occupational Medicine, Łódz, Poland</nlm:aff></affiliation></author><author><name sortKey="Allen, Steve" sort="Allen, Steve" uniqKey="Allen S" first="Steve" last="Allen">Steve Allen</name><affiliation><nlm:aff id="Aff39">Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK</nlm:aff></affiliation></author><author><name sortKey="Vinnicombe, Sarah" sort="Vinnicombe, Sarah" uniqKey="Vinnicombe S" first="Sarah" last="Vinnicombe">Sarah Vinnicombe</name><affiliation><nlm:aff id="Aff40">Division of Cancer Research, Ninewells Hospital & Medical School, Dundee, UK</nlm:aff></affiliation></author><author><name sortKey="Moss, Sue" sort="Moss, Sue" uniqKey="Moss S" first="Sue" last="Moss">Sue Moss</name><affiliation><nlm:aff id="Aff41">Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK</nlm:aff></affiliation></author><author><name sortKey="Chiarelli, Anna M" sort="Chiarelli, Anna M" uniqKey="Chiarelli A" first="Anna M." last="Chiarelli">Anna M. Chiarelli</name><affiliation><nlm:aff id="Aff42">Ontario Breast Screening Program, Cancer Care Ontario, Toronto, Canada</nlm:aff></affiliation></author><author><name sortKey="Linton, Linda" sort="Linton, Linda" uniqKey="Linton L" first="Linda" last="Linton">Linda Linton</name><affiliation><nlm:aff id="Aff43">Princess Margaret Cancer Centre, Toronto, Canada</nlm:aff></affiliation></author><author><name sortKey="Maskarinec, Gertraud" sort="Maskarinec, Gertraud" uniqKey="Maskarinec G" first="Gertraud" last="Maskarinec">Gertraud Maskarinec</name><affiliation><nlm:aff id="Aff44">University of Hawaii Cancer Center, Honolulu, HI USA</nlm:aff></affiliation></author><author><name sortKey="Yaffe, Martin J" sort="Yaffe, Martin J" uniqKey="Yaffe M" first="Martin J." last="Yaffe">Martin J. Yaffe</name><affiliation><nlm:aff id="Aff45">Medical Biophysics, University of Toronto, Toronto, Canada</nlm:aff></affiliation></author><author><name sortKey="Boyd, Norman F" sort="Boyd, Norman F" uniqKey="Boyd N" first="Norman F." last="Boyd">Norman F. Boyd</name><affiliation><nlm:aff id="Aff43">Princess Margaret Cancer Centre, Toronto, Canada</nlm:aff></affiliation></author><author><name sortKey="Dos Santos Silva, Isabel" sort="Dos Santos Silva, Isabel" uniqKey="Dos Santos Silva I" first="Isabel" last="Dos-Santos-Silva">Isabel Dos-Santos-Silva</name><affiliation><nlm:aff id="Aff46">Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK</nlm:aff></affiliation></author><author><name sortKey="Mccormack, Valerie A" sort="Mccormack, Valerie A" uniqKey="Mccormack V" first="Valerie A." last="Mccormack">Valerie A. Mccormack</name><affiliation><nlm:aff id="Aff1">Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France</nlm:aff></affiliation></author></analytic><series><title level="j">Breast Cancer Research : BCR</title><idno type="ISSN">1465-5411</idno><idno type="eISSN">1465-542X</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Inter-women and intra-women comparisons of mammographic density (MD) are needed in research, clinical and screening applications; however, MD measurements are influenced by mammography modality (screen film/digital) and digital image format (raw/processed). We aimed to examine differences in MD assessed on these image types.</p></sec><sec><title>Methods</title><p>We obtained 1294 pairs of images saved in both raw and processed formats from Hologic and General Electric (GE) direct digital systems and a Fuji computed radiography (CR) system, and 128 screen-film and processed CR-digital pairs from consecutive screening rounds. Four readers performed Cumulus-based MD measurements (<italic>n</italic> = 3441), with each image pair read by the same reader. Multi-level models of square-root percent MD were fitted, with a random intercept for woman, to estimate processed–raw MD differences.</p></sec><sec><title>Results</title><p>Breast area did not differ in processed images compared with that in raw images, but the percent MD was higher, due to a larger dense area (median 28.5 and 25.4 cm<sup>2</sup> respectively, mean √dense area difference 0.44 cm (95% CI: 0.36, 0.52)). This difference in √dense area was significant for direct digital systems (Hologic 0.50 cm (95% CI: 0.39, 0.61), GE 0.56 cm (95% CI: 0.42, 0.69)) but not for Fuji CR (0.06 cm (95% CI: −0.10, 0.23)). Additionally, within each system, reader-specific differences varied in magnitude and direction (<italic>p</italic> < 0.001). Conversion equations revealed differences converged to zero with increasing dense area. MD differences between screen-film and processed digital on the subsequent screening round were consistent with expected time-related MD declines.</p></sec><sec><title>Conclusions</title><p>MD was slightly higher when measured on processed than on raw direct digital mammograms. Comparisons of MD on these image formats should ideally control for this non-constant and reader-specific difference.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s13058-016-0787-0) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Boyd, Nf" uniqKey="Boyd N">NF Boyd</name></author><author><name sortKey="Guo, H" uniqKey="Guo H">H Guo</name></author><author><name sortKey="Martin, Lj" uniqKey="Martin L">LJ Martin</name></author><author><name sortKey="Sun, L" uniqKey="Sun L">L Sun</name></author><author><name sortKey="Stone, J" uniqKey="Stone J">J Stone</name></author><author><name sortKey="Fishell, E" uniqKey="Fishell E">E Fishell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Warwick, J" uniqKey="Warwick J">J Warwick</name></author><author><name 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contrib-type="author"><name><surname>Moss</surname><given-names>Sue</given-names></name><address><email>s.moss@qmul.ac.uk</email></address><xref ref-type="aff" rid="Aff41">41</xref></contrib><contrib contrib-type="author"><name><surname>Chiarelli</surname><given-names>Anna M.</given-names></name><address><email>anna.chiarelli@utoronto.ca</email></address><xref ref-type="aff" rid="Aff42">42</xref></contrib><contrib contrib-type="author"><name><surname>Linton</surname><given-names>Linda</given-names></name><address><email>llinton@uhnres.utoronto.ca</email></address><xref ref-type="aff" rid="Aff43">43</xref></contrib><contrib contrib-type="author"><name><surname>Maskarinec</surname><given-names>Gertraud</given-names></name><address><email>gertraud@cc.hawaii.edu</email></address><xref ref-type="aff" rid="Aff44">44</xref></contrib><contrib contrib-type="author"><name><surname>Yaffe</surname><given-names>Martin J.</given-names></name><address><email>martin.yaffe@sri.utoronto.ca</email></address><xref ref-type="aff" rid="Aff45">45</xref></contrib><contrib contrib-type="author"><name><surname>Boyd</surname><given-names>Norman F.</given-names></name><address><email>boyd@uhnres.utoronto.ca</email></address><xref ref-type="aff" rid="Aff43">43</xref></contrib><contrib contrib-type="author"><name><surname>dos-Santos-Silva</surname><given-names>Isabel</given-names></name><address><email>Isabel.Silva@lshtm.ac.uk</email></address><xref ref-type="aff" rid="Aff46">46</xref></contrib><contrib contrib-type="author"><name><surname>McCormack</surname><given-names>Valerie A.</given-names></name><address><email>McCormackV@iarc.fr</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><aff id="Aff1"><label>1</label>Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France</aff><aff id="Aff2"><label>2</label>Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Australia</aff><aff id="Aff3"><label>3</label>Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA</aff><aff id="Aff4"><label>4</label>Moffitt Cancer Center, Tampa, FL USA</aff><aff id="Aff5"><label>5</label>Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA</aff><aff id="Aff6"><label>6</label>Department of Surgery, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey</aff><aff id="Aff7"><label>7</label>Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile</aff><aff id="Aff8"><label>8</label>Centre for Medical Image Computing, University College London, London, UK</aff><aff id="Aff9"><label>9</label>Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa</aff><aff id="Aff10"><label>10</label>Marmara University School of Medicine Department of Radiology, Istanbul, Turkey</aff><aff id="Aff11"><label>11</label>Slone Epidemiology Center, Boston University, Boston, MA USA</aff><aff id="Aff12"><label>12</label>Division of Breast Surgery, Department of Surgery, The University of Hong Kong, Hong Kong, People’s Republic of China</aff><aff id="Aff13"><label>13</label>Department of Surgery, Hong Kong Sanatorium and Hospital, Hong Kong, People’s Republic of China</aff><aff id="Aff14"><label>14</label>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria Australia</aff><aff id="Aff15"><label>15</label>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria Australia</aff><aff id="Aff16"><label>16</label>Cancer Epidemiology Unit, Instituto de Salud Carlos III and CIBERESP, Madrid, Spain</aff><aff id="Aff17"><label>17</label>Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia</aff><aff id="Aff18"><label>18</label>Cancer Research Malaysia, Subang Jaya, Malaysia</aff><aff id="Aff19"><label>19</label>Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA USA</aff><aff id="Aff20"><label>20</label>Center for Research on Population Health, Instituto Nacional de Salud Pública, Mexico City, Mexico</aff><aff id="Aff21"><label>21</label>Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France</aff><aff id="Aff22"><label>22</label>National Cancer Control Center, Haifa, Israel</aff><aff id="Aff23"><label>23</label>Cancer Registry of Norway, Oslo, Norway</aff><aff id="Aff24"><label>24</label>Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway</aff><aff id="Aff25"><label>25</label>Department of Preventive Medicine, University of Southern California, Los Angeles, CA USA</aff><aff id="Aff26"><label>26</label>Norwegian Center for Minority and Migrant Health Research (NAKMI), Oslo, Norway</aff><aff id="Aff27"><label>27</label>Department of Population Sciences, Beckman Research Institute, City of Hope, CA USA</aff><aff id="Aff28"><label>28</label>Isfahan University of Medical Sciences, Isfahan, Iran</aff><aff id="Aff29"><label>29</label>Department of Surgery, Asan Medical Center, Seoul, Republic of Korea</aff><aff id="Aff30"><label>30</label>Cairo University, Cairo, Egypt</aff><aff id="Aff31"><label>31</label>Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt</aff><aff id="Aff32"><label>32</label>Department of Surgery, Yong Loo Lin School of Medicine, Singapore, Singapore</aff><aff id="Aff33"><label>33</label>Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore</aff><aff id="Aff34"><label>34</label>NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore</aff><aff id="Aff35"><label>35</label>Gifu University, Gifu, Japan</aff><aff id="Aff36"><label>36</label>Aga Khan University Hospital, Nairobi, Kenya</aff><aff id="Aff37"><label>37</label>Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands</aff><aff id="Aff38"><label>38</label>Nofer Institute of Occupational Medicine, Łódz, Poland</aff><aff id="Aff39"><label>39</label>Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK</aff><aff id="Aff40"><label>40</label>Division of Cancer Research, Ninewells Hospital & Medical School, Dundee, UK</aff><aff id="Aff41"><label>41</label>Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK</aff><aff id="Aff42"><label>42</label>Ontario Breast Screening Program, Cancer Care Ontario, Toronto, Canada</aff><aff id="Aff43"><label>43</label>Princess Margaret Cancer Centre, Toronto, Canada</aff><aff id="Aff44"><label>44</label>University of Hawaii Cancer Center, Honolulu, HI USA</aff><aff id="Aff45"><label>45</label>Medical Biophysics, University of Toronto, Toronto, Canada</aff><aff id="Aff46"><label>46</label>Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK</aff></contrib-group><pub-date pub-type="epub"><day>19</day><month>12</month><year>2016</year></pub-date><pub-date pub-type="pmc-release"><day>19</day><month>12</month><year>2016</year></pub-date><pub-date pub-type="ppub"><year>2016</year></pub-date><volume>18</volume><elocation-id>130</elocation-id><history><date date-type="received"><day>30</day><month>8</month><year>2016</year></date><date date-type="accepted"><day>23</day><month>11</month><year>2016</year></date></history><permissions><copyright-statement>© The Author(s). 2016</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold>This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p>Inter-women and intra-women comparisons of mammographic density (MD) are needed in research, clinical and screening applications; however, MD measurements are influenced by mammography modality (screen film/digital) and digital image format (raw/processed). We aimed to examine differences in MD assessed on these image types.</p></sec><sec><title>Methods</title><p>We obtained 1294 pairs of images saved in both raw and processed formats from Hologic and General Electric (GE) direct digital systems and a Fuji computed radiography (CR) system, and 128 screen-film and processed CR-digital pairs from consecutive screening rounds. Four readers performed Cumulus-based MD measurements (<italic>n</italic> = 3441), with each image pair read by the same reader. Multi-level models of square-root percent MD were fitted, with a random intercept for woman, to estimate processed–raw MD differences.</p></sec><sec><title>Results</title><p>Breast area did not differ in processed images compared with that in raw images, but the percent MD was higher, due to a larger dense area (median 28.5 and 25.4 cm<sup>2</sup> respectively, mean √dense area difference 0.44 cm (95% CI: 0.36, 0.52)). This difference in √dense area was significant for direct digital systems (Hologic 0.50 cm (95% CI: 0.39, 0.61), GE 0.56 cm (95% CI: 0.42, 0.69)) but not for Fuji CR (0.06 cm (95% CI: −0.10, 0.23)). Additionally, within each system, reader-specific differences varied in magnitude and direction (<italic>p</italic> < 0.001). Conversion equations revealed differences converged to zero with increasing dense area. MD differences between screen-film and processed digital on the subsequent screening round were consistent with expected time-related MD declines.</p></sec><sec><title>Conclusions</title><p>MD was slightly higher when measured on processed than on raw direct digital mammograms. Comparisons of MD on these image formats should ideally control for this non-constant and reader-specific difference.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s13058-016-0787-0) contains supplementary material, which is available to authorized users.</p></sec></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Breast density</kwd><kwd>Image processing</kwd><kwd>Mammographic density assessment</kwd><kwd>Breast cancer</kwd><kwd>Methods</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100000002</institution-id><institution>National Institutes of Health</institution></institution-wrap></funding-source><award-id>R03CA167771</award-id></award-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100008700</institution-id><institution>Centre International de Recherche sur le Cancer</institution></institution-wrap></funding-source></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2016</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="Sec1"><title>Background</title><p>Mammographic density (MD), a measure of the radiodense tissue in the breast, is a strong marker of breast cancer (BC) risk [<xref ref-type="bibr" rid="CR1">1</xref>]. MD is increasingly being incorporated into BC research and clinical practice, for example in BC risk prediction models [<xref ref-type="bibr" rid="CR2">2</xref>], as a marker for the effectiveness of therapeutic drugs mediated through MD [<xref ref-type="bibr" rid="CR3">3</xref>], and in risk-based stratification for tailored BC screening regimens [<xref ref-type="bibr" rid="CR4">4</xref>]. To enable these applications, estimates of differences in MD between women and within women over time are needed. However, obtaining directly comparable MD measurements is challenged by the fact that no single MD measurement tool is used universally; there are more than 10 quantitative methods currently in use [<xref ref-type="bibr" rid="CR5">5</xref>–<xref ref-type="bibr" rid="CR8">8</xref>]. Further, for the widely used threshold method, MD measurements are affected by well-documented reader variability [<xref ref-type="bibr" rid="CR9">9</xref>, <xref ref-type="bibr" rid="CR10">10</xref>]. Less studied is the influence of the type of mammogram used for MD measurements. Images originate from a variety of imaging modalities and mammography systems; that is, from older screen-film mammography (SFM) or more recently from digital mammography.</p><p>Image quality differs between SFM and digital mammography—for example, in terms of object visibility and spatial resolution [<xref ref-type="bibr" rid="CR11">11</xref>]—and thus a reader’s assessment of threshold-based MD may also differ between these modalities. Further, digital images are acquired in a raw (‘for processing’) format, in which the greyscale is proportional to X-ray attenuation. The processed (‘for presentation’) image is a manipulation of the raw image to aid tumour detection, based on manufacturer-specific algorithms which are generally unspecified and thus irreversible. Because processing may suppress or enhance image features such as dense tissue, MD measurements may systematically differ between the original raw and the processed images. The raw image is often deleted and only a processed format is available for MD measurements. Further, differences in MD between raw and processed images may vary by the type of digital mammography; that is, computed radiography (CR, a digital extension of screen film) or direct digital.</p><p>Two previous studies of MD in raw–processed pairs showed different results. From a General Electric (GE) Senographe 2000D model, percent MD (PMD) was higher in processed than in raw images [<xref ref-type="bibr" rid="CR12">12</xref>]; whereas on images captured on a GE Senographe DS model [<xref ref-type="bibr" rid="CR10">10</xref>], PMD was lower in processed than in raw images for one reader, but not different for another reader. We are not aware of raw–processed MD comparisons for other mammography systems.</p><p>In the present study, we extended the examination of MD across three widely used digital mammography systems (GE and Hologic, both direct digital, and Fuji, a CR system) by comparing threshold-based MD measurements for the same mammogram saved in both raw and processed formats and estimating MD conversion equations between these formats. In a similar fashion, we examined differences in MD between digitized SFM and processed CR-digital images taken from the same woman during consecutive screening rounds.</p></sec><sec id="Sec2"><title>Methods</title><sec id="Sec3"><title>Source of images</title><p>For raw–processed MD comparisons, we included women who had both raw and processed image pairs available; that is, the same mammogram from a single screening session was saved in both formats. To examine different digital mammography system manufacturers (hereafter ‘systems’) we acquired six sets from three systems (Table <xref rid="Tab1" ref-type="table">1</xref>): two direct digital systems (Hologic: sets H1, H2 and H3; and GE: sets G1 and G2) and a Fuji CR system (set F1). Hologic images were all captured on Lorad Selenia models whereas the GE images were captured on different models; Senographe 2000D, DS and Essential. Image sets originated from the Chilean Cohort Study of Breast Cancer Risk [<xref ref-type="bibr" rid="CR13">13</xref>] (set H1), the Bahcesehir Mammographic Screening project in Turkey [<xref ref-type="bibr" rid="CR14">14</xref>] (set H2), screening mammograms from the H. Lee Moffitt Cancer Center, Florida, USA (sets H3 and G1) [<xref ref-type="bibr" rid="CR12">12</xref>] and the East London Breast Screening Programme, UK (set G2) [<xref ref-type="bibr" rid="CR7">7</xref>]. These five sets reflect populations with nearly 3-fold differences in BC incidence rates [<xref ref-type="bibr" rid="CR15">15</xref>]. In contrast, set F1 is a pooled resource of anonymized Fuji CR images taken for 100 women in 2008, on which both right craniocaudal (CC) and left CC images were saved in both formats (400 images). Other than age for 47 women, no other information was known about these women. Thus whilst all other sets were from BC-free women, we cannot guarantee this status for set F1. All mammograms were taken between 2007 and 2013. Two sets, H1 and G2, also contributed to the International Consortium on Mammographic Density (ICMD) [<xref ref-type="bibr" rid="CR16">16</xref>].<table-wrap id="Tab1"><label>Table 1</label><caption><p>Characteristics of mammograms and of women with raw–processed image pairs and SFM–digital image pairs</p></caption><table frame="hsides" rules="groups"><thead><tr><th></th><th colspan="6">Raw–processed image pairs</th><th>Processed digital–SFM pairs</th></tr><tr><th></th><th>Set H1</th><th>Set H2</th><th>Set H3</th><th>Set G1</th><th>Set G2</th><th>Set F1</th><th>Set F2</th></tr></thead><tbody><tr><td>Mammography system</td><td colspan="3">Hologic (DD)</td><td colspan="2">GE Medical Systems (DD)</td><td>Fuji (CR)</td><td>Fuji (SFM and CR)</td></tr><tr><td>Mammography machine</td><td colspan="3">Lorad Selenia</td><td>Senographe 2000D</td><td>Senographe Essential (152 pairs), Senographe DS (87 pairs)</td><td>Clearview CSm</td><td>–</td></tr><tr><td>Views</td><td>L MLO</td><td>L MLO</td><td>L CC or R CC</td><td>L CC or R CC</td><td>L MLO</td><td>L CC and R CC</td><td>R CC</td></tr><tr><td>Pixel size (μm)</td><td>70</td><td>70</td><td>NK</td><td>NK</td><td>94 (91%), 100 (9%)</td><td>50</td><td>SFM: 50 (33%), 200 (67%); CR 50 (50%), 100 (50%)</td></tr><tr><td>Processing software version</td><td>AWS 3_3_1</td><td>AWS 3_4_1</td><td>NK</td><td>NK</td><td>ADS_43.10.1 (34.2%), ADS_54.10 (56.9%), ADS_54.11 (8.9%)</td><td></td><td></td></tr><tr><td>Number of image pairs</td><td>186</td><td>73</td><td>417</td><td>180</td><td>238</td><td>200</td><td>139</td></tr><tr><td>Number of women</td><td>186</td><td>73</td><td>417</td><td>180</td><td>238</td><td>100</td><td>139</td></tr><tr><td>Source of films</td><td>Chilean Cohort Study of Breast Cancer Risk (in ICMD)</td><td>Bahcesehir screening programme, Turkey</td><td>H. Lee Moffitt Cancer Centre, USA</td><td>H. Lee Moffitt Cancer Centre, USA</td><td>East London Breast Screening Centre, UK (in ICMD)</td><td>NK</td><td>BreastScreen Victoria, Australia</td></tr><tr><td>Year<sup>a</sup></td><td>2011–2013</td><td>2010–2011</td><td>2008–2010</td><td>2007–2011</td><td>2010–2012</td><td>2008</td><td>2004–2009</td></tr><tr><td>Age<sup>a</sup> (years), mean (SD)</td><td>41.0 (4.4)</td><td>49.5 (7.5)</td><td>63.5 (10.7)</td><td>58.5 (10.4)</td><td>58.0 (5.8)</td><td>55.1 (12.8)<sup>b</sup></td><td>57.9 (5.1) first screen, 60.0 (5.1) second</td></tr><tr><td>BMI<sup>c</sup> (kg/m<sup>2</sup>), median (IQR)</td><td>27.6 (24.9–32.1)</td><td>NK</td><td>27.6 (24.3–32.4)</td><td>24.7 (22.3–27.0)</td><td>24.6 (22.5–28.8)</td><td>NK</td><td>NK</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>At the time of mammography</p><p><sup>b</sup>Age was known for 47 of 100 women only. Set F1: both R CC and L CC images were saved in raw and processed formats, therefore there are 100 women and 200 image pairs</p><p><sup>c</sup>BMI at or near to mammography</p><p><italic>L</italic> left, <italic>R</italic> right, <italic>CC</italic> craniocaudal, <italic>MLO</italic> mediolateral oblique, <italic>GE</italic> General Electric, <italic>SFM</italic> screen-film mammography, <italic>DD</italic> direct digital, <italic>CR</italic> computed radiography, <italic>IMCD</italic> International Consortium on Mammographic Density, <italic>IQR</italic> interquartile range, <italic>NK</italic> not known, <italic>SD</italic> standard deviation</p></table-wrap-foot></table-wrap></p><p>For the comparison of MD assessed on SFM and digital mammography (Table <xref rid="Tab1" ref-type="table">1</xref>, set F2, BreastScreen Victoria, Australia), we obtained pairs of view and laterality-matched films for the same 139 woman who were screened on SFM at one screening round and on a digital CR Fuji system at the next, a median of 2.1 years later (range 1.2–2.5 years).</p><p>Ethics approvals were obtain from IARC (IEC 12–34 for the ICMD) and from contributing studies.</p></sec><sec id="Sec4"><title>MD measurements</title><p>To improve readability of raw images, greyscale levels were transformed using a log-inversion implemented in Niftyview [<xref ref-type="bibr" rid="CR17">17</xref>]. This process creates a ‘positive’ image out of the raw ‘negative’ and restores the approximately linear relationship between image intensity and tissue density exhibited by SFM. MD was measured in Cumulus version 3 or 6, in which the reader selects the threshold to dichotomize dense and non-dense pixels. These versions give equivalent MD measurements, but differ in ease of use for the reader. Measures obtained are areas (cm<sup>2</sup>) of the breast, the dense area (DA) and the non-dense area, and PMD, calculated as:<disp-formula id="Equa"><alternatives><tex-math id="M1">\documentclass[12pt]{minimal}
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\begin{document}$$ \mathrm{P}\mathrm{M}\mathrm{D} = 100 \times \mathrm{D}\mathrm{A}\ /\ \mathrm{breast}\ \mathrm{area}. $$\end{document}</tex-math><mml:math id="M2"><mml:mi mathvariant="normal">P</mml:mi><mml:mi mathvariant="normal">M</mml:mi><mml:mi mathvariant="normal">D</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mo>=</mml:mo><mml:mspace width="0.25em"></mml:mspace><mml:mn>100</mml:mn><mml:mspace width="0.25em"></mml:mspace><mml:mo>×</mml:mo><mml:mspace width="0.25em"></mml:mspace><mml:mi mathvariant="normal">D</mml:mi><mml:mi mathvariant="normal">A</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mo stretchy="true">/</mml:mo><mml:mspace width="0.25em"></mml:mspace><mml:mi mathvariant="normal">breast</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi mathvariant="normal">area</mml:mi><mml:mo>.</mml:mo></mml:math><graphic xlink:href="13058_2016_787_Article_Equa.gif" position="anchor"></graphic></alternatives></disp-formula></p><p>Image sets were read by four experienced readers (VAM, Id-S-S, NFB and JH) in combinations dependent on permissions for inter-institutional image transfers. Sets H1, H2 and G2 were distributed randomly into 12 batches of 100 images (six raw and six processed batches) and allocated randomly to three readers. Each pair was read by the same reader. Each batch included three within-batch repeats and five images from each batch were repeated in the other two readers’ batches. The Fuji images (F1) and the SFM-digital image set (F2) were mainly read by a single reader. Sets H3 and G1 were not transferred between institutions, but had been measured previously by one reader as published previously [<xref ref-type="bibr" rid="CR12">12</xref>].</p><p>Twelve image pairs were excluded because one or both images were indicated for exclusion upon MD measurement (e.g. due to low image quality, breast implants).</p></sec><sec id="Sec5"><title>Statistical methods</title><p>The primary outcome is PMD (%), and secondary outcomes are DA and breast area. For each of these, we used a square-root transformation (e.g. √PMD) to normalize distributions [<xref ref-type="bibr" rid="CR18">18</xref>]. The interpretation of these measures can be aided by considering each area as a square, thus √DA and √breast area are the width in centimetres of the square. Similarly, √PMD can be thought of as the width of the dense square for a 10 cm × 10 cm breast area.</p><p>For each image format, within-reader reliability of √MD was assessed using the intraclass correlation coefficient:<disp-formula id="Equb"><alternatives><tex-math id="M3">\documentclass[12pt]{minimal}
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\begin{document}$$ {{\mathrm{ICC} = \upsigma}^2}_{\mathrm{b}}/\left({\upsigma^2}_{\mathrm{b}}{{ + \upsigma}^2}_{\mathrm{w}}\right). $$\end{document}</tex-math><mml:math id="M4"><mml:msub><mml:msup><mml:mrow><mml:mi mathvariant="normal">I</mml:mi><mml:mi mathvariant="normal">C</mml:mi><mml:mi mathvariant="normal">C</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mo>=</mml:mo><mml:mspace width="0.25em"></mml:mspace><mml:mi mathvariant="normal">σ</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mi mathvariant="normal">b</mml:mi></mml:msub><mml:mo stretchy="true">/</mml:mo><mml:mfenced close=")" open="("><mml:mrow><mml:msub><mml:msup><mml:mi mathvariant="normal">σ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mi mathvariant="normal">b</mml:mi></mml:msub><mml:msub><mml:msup><mml:mrow><mml:mo>+</mml:mo><mml:mspace width="0.25em"></mml:mspace><mml:mi mathvariant="normal">σ</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mi mathvariant="normal">w</mml:mi></mml:msub></mml:mrow></mml:mfenced><mml:mo>.</mml:mo></mml:math><graphic xlink:href="13058_2016_787_Article_Equb.gif" position="anchor"></graphic></alternatives></disp-formula></p><p>Between-women variance (σ<sup>2</sup><sub>b</sub>) and within-reader variance (σ<sup>2</sup><sub>w</sub>) were estimated in ANOVA models fitted on sets H1, H2, G2, F1 and F2 and all of the ICMD measurements combined. Sets H3 and G1 did not have within-reader repeats.</p><p>To estimate within-pair raw–processed differences in MD, we fitted multi-level normal-error regression models of √MD, where the fixed effect of image format was level 1 and a random intercept for woman was level 2. The assumption of a constant difference in √MD across the MD range was examined using Bland–Altman plots. Subgroup analyses were conducted by reader, system, model and processing software version, and by PMD and breast area categories and possible effect modification tested using likelihood ratio tests. These potential effect modifiers are features of the image or of the imaging process; woman-level characteristics such as body mass index (BMI) or age were not investigated, because potential effect modification would be mediated through image characteristics.</p><p>A similar approach was used to compare SFM and digital processed images for set F2.</p><p>Calibration equations for conversion between MD measured on raw and processed images, and vice versa, were based on √DA because all √PMD differences were driven through √DA whilst the change in √breast area was negligible (<1 mm). Standard regression models were not used as they assume error only in the dependent variable, which results in a fitted model that is not reversible (i.e. predicting raw from processed would give a different outcome to predicting processed from raw). Because there is measurement error in MD assessment on both raw and processed films, we applied a reversible conversion method. The principle of this calibration method was to maintain, for each reader and system combination, equality of the standard normal <italic>z</italic> scores of √DA whether they were assessed on a processed image (<italic>z</italic><sub>p</sub>) or a raw image (<italic>z</italic><sub>r</sub>):<disp-formula id="Equc"><alternatives><tex-math id="M5">\documentclass[12pt]{minimal}
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\begin{document}$$ {z}_r=\left(\surd D{A}_r{\textstyle \hbox{-} }{\overline{x}}_r\right)/{s}_r, $$\end{document}</tex-math><mml:math id="M8" display="block"><mml:msub><mml:mrow><mml:mi>z</mml:mi></mml:mrow><mml:mi>r</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mo stretchy="false">√</mml:mo><mml:mi>D</mml:mi><mml:msub><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mi>r</mml:mi></mml:msub><mml:mstyle displaystyle="false" scriptlevel="0"><mml:mtext>-</mml:mtext></mml:mstyle><mml:msub><mml:mrow><mml:mover accent="true"><mml:mi>x</mml:mi><mml:mo stretchy="true">¯</mml:mo></mml:mover></mml:mrow><mml:mi>r</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mo>/</mml:mo></mml:mrow><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mi>r</mml:mi></mml:msub><mml:mo>,</mml:mo></mml:math><graphic xlink:href="13058_2016_787_Article_Equd.gif" position="anchor"></graphic></alternatives></disp-formula>where <italic>x̄</italic> and <italic>s</italic> are the mean and standard deviation for the image type respectively. This method yields the following conversion equation:<disp-formula id="Eque"><alternatives><tex-math id="M9">\documentclass[12pt]{minimal}
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\begin{document}$$ \surd D{A}_r={\overline{x}}_r+{s}_r{z}_p. $$\end{document}</tex-math><mml:math id="M10"><mml:mo>√</mml:mo><mml:mi>D</mml:mi><mml:msub><mml:mi>A</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mover accent="true"><mml:mi>x</mml:mi><mml:mo stretchy="true">¯</mml:mo></mml:mover><mml:mi>r</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>s</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:msub><mml:mi>z</mml:mi><mml:mi>p</mml:mi></mml:msub><mml:mo>.</mml:mo></mml:math><graphic xlink:href="13058_2016_787_Article_Eque.gif" position="anchor"></graphic></alternatives></disp-formula></p></sec></sec><sec id="Sec6"><title>Results</title><p>In total, 1294 raw–processed digital image pairs (2588 images) were analysed: 676 pairs captured on Hologic Lorad Selenia direct digital systems (CC and mediolateral oblique (MLO)), 418 on GE Senographe direct digital systems (CC and MLO) and 200 from Fuji CR (CC only) (Table <xref rid="Tab1" ref-type="table">1</xref>). For digital image pairs, women were aged from 26 to 87 years at mammography (mean 55.1, SD 12.8) and the median BMI was 26.2 kg/m<sup>2</sup> but varied between sets. Median overall PMD ranged between 15.4 and 24.8% and median DA ranged between 23.6 and 30.4 cm<sup>2</sup> (Table <xref rid="Tab2" ref-type="table">2</xref>) and reader-specific median measures are given in (Additional file <xref rid="MOESM1" ref-type="media">1</xref>: Table S1). Visual examination of sample raw–processed image pairs shows different degrees of accentuation of breast features and of the skin edge (Fig. <xref rid="Fig1" ref-type="fig">1</xref>).<table-wrap id="Tab2"><label>Table 2</label><caption><p>Percent density, dense area and total breast area in raw–processed image pairs and in SFM–processed digital image pairs</p></caption><table frame="hsides" rules="groups"><thead><tr><th rowspan="2" colspan="2"></th><th colspan="4">Raw–processed image pairs</th><th>SFM–digital</th></tr><tr><th>Hologic</th><th>GE</th><th>Fuji</th><th>All</th><th>Fuji CR</th></tr></thead><tbody><tr><td>Number of women</td><td></td><td>676</td><td>418</td><td>100</td><td>1194</td><td>128</td></tr><tr><td>Number of image pairs</td><td></td><td>676</td><td>418</td><td>200</td><td>1294</td><td>128</td></tr><tr><td rowspan="4">Number of image pairs by view</td><td>L MLO</td><td>259</td><td>238</td><td>0</td><td>497</td><td></td></tr><tr><td>L CC</td><td>208</td><td>79</td><td>100</td><td>387</td><td></td></tr><tr><td>R CC</td><td>209</td><td>101</td><td>100</td><td>410</td><td>128</td></tr><tr><td>All</td><td>676</td><td>418</td><td>200</td><td>1294</td><td>128</td></tr><tr><td rowspan="5">Number of potential MD readings (including 22% repeats), by reader</td><td>Reader 1</td><td>234</td><td>232</td><td>60</td><td>526</td><td>0</td></tr><tr><td>Reader 2</td><td>246</td><td>218</td><td>60</td><td>524</td><td>0</td></tr><tr><td>Reader 3</td><td>232</td><td>222</td><td>460</td><td>914</td><td>283</td></tr><tr><td>Reader 4</td><td>834</td><td>360</td><td>0</td><td>1194</td><td>0</td></tr><tr><td>All</td><td>1546</td><td>1032</td><td>580</td><td>3158</td><td>283</td></tr><tr><td>PMD<sup>a</sup> (%)</td><td>Raw</td><td>15.4 (6.7–27.7)</td><td>18.5 (8.5–32)</td><td>23.1 (12.5–34.3)</td><td>18.1 (8.6–30.5)</td><td>SFM: 22.2 (15.6–28.5)</td></tr><tr><td></td><td>Processed</td><td>18.7 (11.4–27.9)</td><td>21.8 (11.3–35.7)</td><td>24.8 (13.4–36.6)</td><td>20.2 (11.7–31.7)</td><td>18.9 (13.0–26.9)</td></tr><tr><td>Dense area<sup>a</sup> (cm<sup>2</sup>)</td><td>Raw</td><td>23.6 (12.1–41.3)</td><td>25.0 (11.7–40.3)</td><td>28.8 (19.9–45.3)</td><td>25.4 (13.5–41.7)</td><td>SFM: 32.4 (22.4–43.2)</td></tr><tr><td></td><td>Processed</td><td>28.2 (19–41.9)</td><td>27.6 (16.1–47.6)</td><td>30.4 (20.3–50.7)</td><td>28.5 (18.2–44.8)</td><td>28.9 (20.3–38.1)</td></tr><tr><td>Breast area<sup>a</sup> (cm<sup>2</sup>)</td><td>Raw</td><td>166.9 (127.9–216.1)</td><td>138.4 (108.4–173.1)</td><td>152.9 (111.4–207.1)</td><td>155.8 (116.9–201.3)</td><td>SFM: 154.4 (119.1–193.1)</td></tr><tr><td></td><td>Processed</td><td>167.3 (127.5–214.4)</td><td>140.1 (109.9–175)</td><td>150.7 (112.7–206.2)</td><td>156.1 (117.3–201.5)</td><td>156.7 (122.7–202.1)</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>Median (interquartile range)</p><p><italic>L</italic> left, <italic>R</italic> right, <italic>CC</italic> craniocaudal, <italic>MLO</italic> mediolateral oblique, <italic>GE</italic> General Electric, <italic>SFM</italic> screen-film mammography, <italic>CR</italic> computed radiography, <italic>PMD</italic> percent mammographic density assessed in Cumulus version 6</p></table-wrap-foot></table-wrap><fig id="Fig1"><label>Fig. 1</label><caption><p>Examples of raw and processed images from Hologic, GE and Fuji digital mammography systems. <bold>a</bold> Raw and <bold>e</bold> processed paired images captured on GE Senographe Essential (G2, UK). <bold>b</bold> Raw and <bold>f</bold> processed paired images captured on Hologic Lorad Selenia (H1, Chile). <bold>c</bold> Raw and <bold>g</bold> processed paired images captured on Fuji CR (F1). <bold>d</bold> Screen-film image and <bold>h</bold> its paired Fujifilm CR processed image (SFM/digital set F2, Australia). <italic>CC</italic> craniocaudal, <italic>L</italic> left, <italic>MLO</italic> mediolateral oblique, <italic>R</italic> right</p></caption><graphic xlink:href="13058_2016_787_Fig1_HTML" id="MO1"></graphic></fig></p><p>Within-reader reliability of PMD was slightly higher in SFM (ICC 0.94, 95% confidence interval (CI): 0.93, 0.95) than in raw digital (ICC 0.91, 95% CI: 0.89, 0.93) or processed digital (ICC 0.89, 95% CI: 0.88, 0.91) images. This difference generally held across readers (Table <xref rid="Tab3" ref-type="table">3</xref>) and was driven by higher within-reader repeatability from SFM than when measuring from digital images. In contrast, whilst readers 1 and 3 had higher ICCs for PMD and DA assessed on raw images than on processed images, this was reversed for reader 2. Reader 1 ICCs for PMD and DA did not differ between image formats for the Fuji CR or Hologic systems, whereas for GE images the ICCs were lower on processed than on raw images. Throughout, ICCs for PMD predominantly reflected those for DA because breast area ICCs were near 100% for all image formats, readers and systems (Table <xref rid="Tab3" ref-type="table">3</xref>). Based on the subset of images that were read by all readers, mean raw-processed MD measures and correlation coefficients by reader are given in Additional file <xref rid="MOESM2" ref-type="media">2</xref>: Table S2 and correlations between readers by image type in (and Additional File <xref rid="MOESM3" ref-type="media">3</xref>: Table S3. <table-wrap id="Tab3"><label>Table 3</label><caption><p>Intra-class correlation coefficient, within-reader and between-woman SD of MD measures to assess repeatability of MD readings, by image format</p></caption><table frame="hsides" rules="groups"><thead><tr><th></th><th colspan="6">SFM</th><th colspan="6">Raw digital</th><th colspan="6">Processed digital</th></tr><tr><th>Measure subset</th><th>N obs</th><th>N women</th><th>N repeats</th><th>ICC</th><th>Within-reader SD</th><th>Between-women SD</th><th>N obs</th><th>N women</th><th>N repeats</th><th>ICC</th><th>Within-reader SD</th><th>Between-women SD</th><th>N obs</th><th>N women</th><th>N repeats</th><th>ICC</th><th>Within-reader SD</th><th>Between-women SD</th></tr></thead><tbody><tr><td colspan="19">Percent mammographic density<sup>a</sup></td></tr><tr><td> All</td><td>6659</td><td>6418</td><td>241</td><td>0.94</td><td>0.42</td><td>1.61</td><td>1243</td><td>1098</td><td>145</td><td>0.91</td><td>0.51</td><td>1.64</td><td>5009</td><td>4627</td><td>394</td><td>0.89</td><td>0.49</td><td>1.40</td></tr><tr><td> Reader 1 (H1, H2, G2, F1)</td><td>1886</td><td>1818</td><td>68</td><td>0.96</td><td>0.38</td><td>1.90</td><td>346</td><td>298</td><td>48</td><td>0.97</td><td>0.33</td><td>1.87</td><td>1539</td><td>1413</td><td>126</td><td>0.91</td><td>0.46</td><td>1.51</td></tr><tr><td> Reader 2 (H1, H2, G2, F1)</td><td>2464</td><td>2381</td><td>83</td><td>0.92</td><td>0.46</td><td>1.55</td><td>356</td><td>309</td><td>47</td><td>0.79</td><td>0.63</td><td>1.24</td><td>1545</td><td>1430</td><td>119</td><td>0.88</td><td>0.48</td><td>1.32</td></tr><tr><td> Reader 3 (H1, H2, G2, F1)</td><td>2309</td><td>2217</td><td>92</td><td>0.89</td><td>0.41</td><td>1.17</td><td>541</td><td>489</td><td>52</td><td>0.87</td><td>0.53</td><td>1.39</td><td>1925</td><td>1775</td><td>150</td><td>0.86</td><td>0.52</td><td>1.31</td></tr><tr><td> Hologic<sup>b</sup> (H1, H2)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>363</td><td>316</td><td>47</td><td>0.86</td><td>0.59</td><td>1.48</td><td>2742</td><td>2517</td><td>225</td><td>0.87</td><td>0.48</td><td>1.25</td></tr><tr><td> GE<sup>b</sup> (G2)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>590</td><td>536</td><td>54</td><td>0.92</td><td>0.54</td><td>1.77</td><td>1234</td><td>1146</td><td>88</td><td>0.87</td><td>0.59</td><td>1.49</td></tr><tr><td> Fuji<sup>b</sup> (F1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>290</td><td>244</td><td>46</td><td>0.94</td><td>0.39</td><td>1.47</td><td>1033</td><td>951</td><td>82</td><td>0.94</td><td>0.39</td><td>1.52</td></tr><tr><td colspan="19">Dense area<sup>a</sup></td></tr><tr><td> All sets, all readers</td><td>6842</td><td>6589</td><td>253</td><td>0.94</td><td>0.48</td><td>1.82</td><td>1244</td><td>1099</td><td>145</td><td>0.88</td><td>0.71</td><td>1.95</td><td>5021</td><td>4616</td><td>393</td><td>0.85</td><td>0.67</td><td>1.59</td></tr><tr><td> Reader 1 (H1, H2, G2, F1)</td><td>1963</td><td>1888</td><td>75</td><td>0.95</td><td>0.45</td><td>2.06</td><td>346</td><td>298</td><td>48</td><td>0.97</td><td>0.40</td><td>2.12</td><td>1543</td><td>1417</td><td>126</td><td>0.89</td><td>0.59</td><td>1.66</td></tr><tr><td> Reader 2 (H1, H2, G2, F1)</td><td>2568</td><td>2482</td><td>86</td><td>0.93</td><td>0.49</td><td>1.81</td><td>357</td><td>310</td><td>47</td><td>0.71</td><td>0.89</td><td>1.39</td><td>1549</td><td>1426</td><td>119</td><td>0.85</td><td>0.64</td><td>1.50</td></tr><tr><td> Reader 3 (H1, H2, G2, F1)</td><td>2311</td><td>2217</td><td>94</td><td>0.89</td><td>0.48</td><td>1.41</td><td>541</td><td>489</td><td>52</td><td>0.84</td><td>0.75</td><td>1.72</td><td>1929</td><td>1778</td><td>151</td><td>0.80</td><td>0.75</td><td>1.47</td></tr><tr><td> Hologic<sup>b</sup> (H1, H2)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>363</td><td>316</td><td>47</td><td>0.84</td><td>0.86</td><td>1.94</td><td>2745</td><td>2520</td><td>225</td><td>0.83</td><td>0.64</td><td>1.43</td></tr><tr><td> GE<sup>b</sup> (G2)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>591</td><td>537</td><td>54</td><td>0.86</td><td>0.75</td><td>1.87</td><td>1243</td><td>1154</td><td>89</td><td>0.77</td><td>0.85</td><td>1.53</td></tr><tr><td> Fuji<sup>b</sup> (F1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>290</td><td>244</td><td>46</td><td>0.94</td><td>0.44</td><td>1.82</td><td>1033</td><td>951</td><td>82</td><td>0.94</td><td>0.50</td><td>1.95</td></tr><tr><td colspan="19">Breast area<sup>a</sup></td></tr><tr><td> All</td><td>6597</td><td>6357</td><td>240</td><td>1.00</td><td>0.14</td><td>2.46</td><td>1243</td><td>1098</td><td>145</td><td>1.00</td><td>0.15</td><td>2.53</td><td>5009</td><td>4616</td><td>393</td><td>1.00</td><td>0.11</td><td>2.76</td></tr><tr><td> Reader 1 (H1, H2, G2, F1)</td><td>1873</td><td>1805</td><td>68</td><td>1.00</td><td>0.10</td><td>2.46</td><td>346</td><td>298</td><td>48</td><td>1.00</td><td>0.06</td><td>2.49</td><td>1539</td><td>1413</td><td>126</td><td>1.00</td><td>0.07</td><td>2.74</td></tr><tr><td> Reader 2 (H1, H2, G2, F1)</td><td>2442</td><td>2359</td><td>83</td><td>0.99</td><td>0.18</td><td>2.47</td><td>356</td><td>309</td><td>47</td><td>1.00</td><td>0.17</td><td>2.48</td><td>1545</td><td>1426</td><td>119</td><td>1.00</td><td>0.10</td><td>2.77</td></tr><tr><td> Reader 3 (H1, H2, G2, F1)</td><td>2282</td><td>2191</td><td>91</td><td>1.00</td><td>0.12</td><td>2.46</td><td>541</td><td>489</td><td>52</td><td>1.00</td><td>0.18</td><td>2.59</td><td>1925</td><td>1775</td><td>150</td><td>1.00</td><td>0.13</td><td>2.76</td></tr><tr><td> Hologic<sup>b</sup> (H1, H2)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>363</td><td>316</td><td>47</td><td>1.00</td><td>0.08</td><td>2.13</td><td>2742</td><td>2517</td><td>225</td><td>1.00</td><td>0.09</td><td>2.54</td></tr><tr><td> GE<sup>b</sup> (G2)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>590</td><td>536</td><td>54</td><td>0.99</td><td>0.19</td><td>2.46</td><td>1234</td><td>1146</td><td>88</td><td>1.00</td><td>0.09</td><td>2.53</td></tr><tr><td> Fuji<sup>b</sup> (F1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>290</td><td>244</td><td>46</td><td>1.00</td><td>0.15</td><td>2.73</td><td>1033</td><td>951</td><td>82</td><td>1.00</td><td>0.15</td><td>3.01</td></tr></tbody></table><table-wrap-foot><p>Analysis: ICCs, within-reader SD and between-women SD were estimated from a one-way ANOVA using all ICMD measurements and sets H1, H2, G2, F1 and F2. Number of repeats is the number of images read at least twice, by the same or different readers. Reader 4 does not appear here because no repeated readings were available for this reader</p><p>Numbers of observations vary by MD measure because only dense area was measured if the breast edge was not visible, and only percent mammographic density if the pixel size was unknown</p><p><sup>a</sup>Analysed on a square-root scale</p><p><sup>b</sup>Within reader, within image type</p><p><italic>Obs</italic> observations, <italic>SFM</italic> Screen-film mammography, <italic>ICC</italic> Intra-class correlation coefficient, <italic>MD</italic> Mammographic density, <italic>SD</italic> Standard deviation, <italic>N</italic> number of, <italic>GE</italic> General Electric</p></table-wrap-foot></table-wrap></p><p>For processed–raw digital image pairs, the median PMD was higher when measured on processed images than on raw images, by 1.7–3.3 absolute percentage points depending on the system (Table <xref rid="Tab2" ref-type="table">2</xref>). Similarly, the median DA was larger by 1.6–4.6 cm<sup>2</sup>, whereas the median breast area was similar. Regression results were similar: √PMD was 0.34 cm (95% CI: 0.28, 0.40) larger in processed images than in raw images, whilst √DA was 0.44 cm (95% CI: 0.36, 0.52) larger and √breast area did not differ (0.01 cm; 95% CI: −0.01, 0.02) (Table <xref rid="Tab4" ref-type="table">4</xref>). These differences in PMD were approximately one-fifth of the between-women SD (Table <xref rid="Tab3" ref-type="table">3</xref>). For a given reader, PMD and DA differences varied in magnitude between systems (heterogeneity <italic>p</italic> < 0.01 for readers 1–3, <italic>p</italic> = 0.21 for reader 4), and for a given system the differences varied in both magnitude and direction between readers (<italic>p</italic> < 0.001 for each system). Specifically, for readers 1, 3 and 4, √PMD was larger in processed than in raw images by 0.4–0.9 cm (reader 1), 0.1–0.7 cm (reader 3) and 0.4–0.6 cm (reader 4), depending on the system. In contrast, √PMD in processed compared with raw images for reader 2 was either not different (GE) or was smaller (Fuji CR system and Hologic). Mean √DA from processed images was 0.9 (95% CI: 0.7, 1.1) higher for reader 2 and 0.9 (95% CI: 0.7, 1.1) higher for reader 3 compared with reader 1. Between-reader differences were larger for raw images; mean √DA was 2.3 (95% CI: 1.9, 2.8) higher for reader 2 and 1.9 (95% CI: 1.4, 2.3) higher for reader 3 compared with reader 1. For SFM, between-reader differences were slightly smaller; mean √DA was 1.3 (95% CI: 1.1, 1.4) higher for reader 2 and 0.7 (95% CI: 0.5, 0.8) higher for reader 3 compared with reader 1. Breast area differences also varied between system–reader combinations, but average differences were extremely small in magnitude (<1.2 mm √breast area). Differences by model or processing software within a system were not significant (data not shown). Effect modification of DA and PMD differences by categories of PMD or of breast area (categories defined by the raw image) were significant (<italic>p</italic> < 0.001 for both). The differences tended to decrease with increasing PMD, but they increased with increasing breast area (Additional File <xref rid="MOESM4" ref-type="media">4</xref>: Table S4).<table-wrap id="Tab4"><label>Table 4</label><caption><p>Mean differences in MD measures between processed images and the corresponding raw digital image, by reader and mammography system</p></caption><table frame="hsides" rules="groups"><thead><tr><th>Reader</th><th>system</th><th>Number of images</th><th>Number of women</th><th colspan="2">Percent density</th><th colspan="2">Dense area</th><th colspan="2">Breast area</th></tr><tr><th></th><th></th><th></th><th></th><th colspan="2">Difference<sup>a</sup> √PMD (95% CI)</th><th colspan="2">Difference<sup>a</sup> √Dense area (cm) (95% CI)</th><th colspan="2">Difference<sup>a</sup> √Breast area (cm) (95% CI)</th></tr></thead><tbody><tr><td colspan="10">Reader 1</td></tr><tr><td></td><td>Hologic</td><td>234</td><td>104</td><td>0.91</td><td>(0.74, 1.08)</td><td>1.17</td><td>(0.96, 1.39)</td><td>0.01</td><td>(−0.03, 0.05)</td></tr><tr><td></td><td>GE</td><td>232</td><td>98</td><td>0.62</td><td>(0.44, 0.80)</td><td>0.79</td><td>(0.57, 1.00)</td><td>0.09</td><td>(0.07, 0.11)</td></tr><tr><td></td><td>Fuji</td><td>60</td><td>15</td><td>0.40</td><td>(0.20, 0.61)</td><td>0.51</td><td>(0.26, 0.75)</td><td>−0.12</td><td>(−0.17, −0.08)</td></tr><tr><td></td><td>All</td><td>526</td><td>217</td><td>0.72</td><td>(0.61, 0.84)</td><td>0.93</td><td>(0.79, 1.06)</td><td>0.03</td><td>(−0.08, 0.84)</td></tr><tr><td></td><td><italic>p</italic> for heterogeneity<sup>b</sup></td><td></td><td></td><td></td><td>0.007</td><td></td><td>0.003</td><td></td><td><0.001</td></tr><tr><td colspan="10">Reader 2</td></tr><tr><td></td><td>Hologic</td><td>246</td><td>109</td><td>−0.47</td><td>(−0.64, −0.30)</td><td>−0.60</td><td>(−0.85, −0.34)</td><td>0.05</td><td>(0.01, 0.09)</td></tr><tr><td></td><td>GE</td><td>218</td><td>95</td><td>0.05</td><td>(−0.12, 0.23)</td><td>0.07</td><td>(−0.15, 0.30)</td><td>0.11</td><td>(0.07, 0.16)</td></tr><tr><td></td><td>Fuji</td><td>60</td><td>15</td><td>−0.76</td><td>(−1.03, −0.48)</td><td>−0.92</td><td>(−1.27, −0.57)</td><td>0.06</td><td>(−0.01, 0.12)</td></tr><tr><td></td><td>All</td><td>524</td><td>219</td><td>−0.28</td><td>(−0.40, −0.17)</td><td>−0.36</td><td>(−0.52, −0.19)</td><td>0.08</td><td>(0.05, 0.11)</td></tr><tr><td></td><td><italic>p</italic> for heterogeneity<sup>b</sup></td><td></td><td></td><td></td><td><0.001</td><td></td><td><0.001</td><td></td><td>0.09</td></tr><tr><td colspan="10">Reader 3</td></tr><tr><td></td><td>Hologic</td><td>232</td><td>98</td><td>0.10</td><td>(−0.04, 0.24)</td><td>0.12</td><td>(−0.07, 0.31)</td><td>0.01</td><td>(−0.03, 0.04)</td></tr><tr><td></td><td>GE</td><td>222</td><td>95</td><td>0.69</td><td>(0.52, 0.85)</td><td>0.88</td><td>(0.64, 1.12)</td><td>0.00</td><td>(−0.03, 0.04)</td></tr><tr><td></td><td>Fuji</td><td>460</td><td>200</td><td>0.10</td><td>(−0.02, 0.23)</td><td>0.13</td><td>(−0.03, 0.29)</td><td>0.03</td><td>(−0.01, 0.08)</td></tr><tr><td></td><td>All</td><td>914</td><td>392</td><td>0.24</td><td>(0.16, 0.33)</td><td>0.31</td><td>(0.20, 0.43)</td><td>0.02</td><td>(0.00, 0.04)</td></tr><tr><td></td><td><italic>p</italic> for heterogeneity<sup>b</sup></td><td></td><td></td><td></td><td><0.001</td><td></td><td><0.001</td><td></td><td>0.48</td></tr><tr><td colspan="10">Reader 4</td></tr><tr><td></td><td>Hologic</td><td>834</td><td>417</td><td>0.55</td><td>(0.44, 0.65)</td><td>0.74</td><td>(0.60, 0.89)</td><td>−0.09</td><td>(−0.10, −0.08)</td></tr><tr><td></td><td>GE</td><td>360</td><td>180</td><td>0.43</td><td>(0.28, 0.58)</td><td>0.50</td><td>(0.34, 0.67)</td><td>0.08</td><td>(0.01, 0.16)</td></tr><tr><td></td><td>All</td><td>1194</td><td>597</td><td>0.51</td><td>(0.43, 0.60)</td><td>0.67</td><td>(0.56, 0.78)</td><td>−0.04</td><td>(−0.07, −0.02)</td></tr><tr><td></td><td><italic>p</italic> for heterogeneity<sup>b</sup></td><td></td><td></td><td></td><td>0.21</td><td></td><td>0.056</td><td></td><td><0.001</td></tr><tr><td colspan="10">All readers combined</td></tr><tr><td></td><td>Hologic</td><td>1546</td><td>679</td><td>0.37</td><td>(0.29, 0.45)</td><td>0.50</td><td>(0.39, 0.61)</td><td>−0.04</td><td>(−0.05, −0.03)</td></tr><tr><td></td><td>GE</td><td>1032</td><td>418</td><td>0.45</td><td>(0.34, 0.56)</td><td>0.56</td><td>(0.42, 0.69)</td><td>0.07</td><td>(0.04, 0.10)</td></tr><tr><td></td><td>Fuji</td><td>580</td><td>200</td><td>0.04</td><td>(−0.09, 0.18)</td><td>0.06</td><td>(−0.10, 0.23)</td><td>0.02</td><td>(−0.03, 0.07)</td></tr><tr><td></td><td>All</td><td>3158</td><td>1297</td><td>0.34</td><td>(0.28, 0.40)</td><td>0.44</td><td>(0.36, 0.52)</td><td>0.01</td><td>(−0.01, 0.02)</td></tr><tr><td></td><td><italic>p</italic> for heterogeneity<sup>b</sup></td><td></td><td></td><td></td><td><0.001</td><td></td><td><0.001</td><td></td><td><0.001</td></tr></tbody></table><table-wrap-foot><p><italic>p</italic> for heterogeneity <0.001 between readers for each of the Hologic, GE and Fuji systems, for both percent density and dense area. For breast area, <italic>p</italic> for heterogeneity <0.001 also between readers on the Hologic system, and no difference between readers for breast area was found for GE (<italic>p</italic> = 0.07) and Fuji (<italic>p</italic> = 0.08)</p><p><sup>a</sup>Differences are processed–raw images</p><p><sup>b</sup><italic>p</italic> value for heterogeneity between systems, for a given reader</p><p><italic>CI</italic> confidence interval, <italic>MD</italic> Mammographic density, <italic>GE</italic> General Electric, <italic>PMD</italic> percent mammographic density</p></table-wrap-foot></table-wrap></p><p>Most scatter plots (Fig. <xref rid="Fig2" ref-type="fig">2</xref>) showed that differences in DA on processed images compared with raw images are larger at lower DAs, and converge towards no difference in breasts with a √DA of ≥5 cm. Bland–Altman plots also revealed that processed–raw differences in √PMD and √DA (Additional File <xref rid="MOESM5" ref-type="media">5</xref>: Figure S1) were not constant across the underlying MD range. However differences were constant on the standardized scale (shown for DA in Additional File <xref rid="MOESM6" ref-type="media">6</xref>: Figure S2), and thus calibration equations were based on standardized values of DA in the two image types. Figure <xref rid="Fig2" ref-type="fig">2</xref> (Additional file <xref rid="MOESM7" ref-type="media">7</xref>: Information 1) presents these reader-specific and system-specific calibration equations for DA. Differences were very small for the Fuji CR and were larger and of a similar magnitude between the direct digital systems. For all readers combined, conversion equations from raw DA to their processed equivalent are as follows:<fig id="Fig2"><label>Fig. 2</label><caption><p>Scatter plot of paired √DA readings measured on processed (<italic>y</italic> axis) vs raw (<italic>x</italic> axis) digital images, by reader and system. <italic>Dashed lines</italic>, equality (if DA from processed images was read identically to raw images); <italic>blue dots</italic>, modelled linear conversion. Reader-specific and system-specific calibration equations for the conversion of raw √DA to processed √DA are supplied in (Additional file <xref rid="MOESM7" ref-type="media">7</xref>: Information 2). <italic>√DA</italic> square root of dense area, <italic>GE</italic> General Electric</p></caption><graphic xlink:href="13058_2016_787_Fig2_HTML" id="MO2"></graphic></fig><list list-type="bullet"><list-item><p>Hologic: processed √DA = 5.252 + 0.719 (raw √DA – 4.751)</p></list-item><list-item><p>GE: processed √DA = 5.081 + 0.872 (raw √DA – 4.523)</p></list-item><list-item><p>Fuji: processed √DA = 5.694 + 1.107 (raw √DA – 5.633)</p></list-item></list></p><p>After correcting DA, the corrected non-dense area and PMD would then be calculated using the original breast area and preserving the original definitions:<disp-formula id="Equf"><alternatives><tex-math id="M11">\documentclass[12pt]{minimal}
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\begin{document}$$ \mathrm{N}\mathrm{o}\mathrm{n}{\textstyle \hbox{-}}\mathrm{dense}\ {\mathrm{area}}_{\mathrm{c}}=\mathrm{breast}\ \mathrm{area}{\textstyle\ \hbox{-}\ }{\mathrm{DA}}_{\mathrm{c}} $$\end{document}</tex-math><mml:math id="M12" display="block"><mml:mrow><mml:mi mathvariant="normal">N</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">o</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">n</mml:mi></mml:mrow><mml:mstyle displaystyle="false" scriptlevel="0"><mml:mtext>-</mml:mtext></mml:mstyle><mml:mrow><mml:mi mathvariant="normal">d</mml:mi><mml:mi mathvariant="normal">e</mml:mi><mml:mi mathvariant="normal">n</mml:mi><mml:mi mathvariant="normal">s</mml:mi><mml:mi mathvariant="normal">e</mml:mi></mml:mrow><mml:mspace width="0.25em"></mml:mspace><mml:msub><mml:mrow><mml:mrow><mml:mi mathvariant="normal">a</mml:mi><mml:mi mathvariant="normal">r</mml:mi><mml:mi mathvariant="normal">e</mml:mi><mml:mi mathvariant="normal">a</mml:mi></mml:mrow></mml:mrow><mml:mrow><mml:mrow><mml:mi mathvariant="normal">c</mml:mi></mml:mrow></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mi mathvariant="normal">b</mml:mi><mml:mi mathvariant="normal">r</mml:mi><mml:mi mathvariant="normal">e</mml:mi><mml:mi mathvariant="normal">a</mml:mi><mml:mi mathvariant="normal">s</mml:mi><mml:mi mathvariant="normal">t</mml:mi></mml:mrow><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mi mathvariant="normal">a</mml:mi><mml:mi mathvariant="normal">r</mml:mi><mml:mi mathvariant="normal">e</mml:mi><mml:mi mathvariant="normal">a</mml:mi></mml:mrow><mml:mstyle displaystyle="false" scriptlevel="0"><mml:mspace width="0.25em"></mml:mspace><mml:mtext>-</mml:mtext><mml:mspace width="0.25em"></mml:mspace></mml:mstyle><mml:msub><mml:mrow><mml:mrow><mml:mi mathvariant="normal">D</mml:mi><mml:mi mathvariant="normal">A</mml:mi></mml:mrow></mml:mrow><mml:mrow><mml:mrow><mml:mi mathvariant="normal">c</mml:mi></mml:mrow></mml:mrow></mml:msub></mml:math><graphic xlink:href="13058_2016_787_Article_Equf.gif" position="anchor"></graphic></alternatives></disp-formula><disp-formula id="Equg"><alternatives><tex-math id="M13">\documentclass[12pt]{minimal}
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\begin{document}$$ {\mathrm{PMD}}_{\mathrm{c}} = 100 \times {\mathrm{DA}}_{\mathrm{c}}/\ \mathrm{breast}\ \mathrm{area}. $$\end{document}</tex-math><mml:math id="M14"><mml:msub><mml:mrow><mml:mi mathvariant="normal">P</mml:mi><mml:mi mathvariant="normal">M</mml:mi><mml:mi mathvariant="normal">D</mml:mi></mml:mrow><mml:mi mathvariant="normal">c</mml:mi></mml:msub><mml:mspace width="0.25em"></mml:mspace><mml:mo>=</mml:mo><mml:mspace width="0.25em"></mml:mspace><mml:mn>100</mml:mn><mml:mspace width="0.25em"></mml:mspace><mml:mo>×</mml:mo><mml:mspace width="0.25em"></mml:mspace><mml:msub><mml:mrow><mml:mi mathvariant="normal">D</mml:mi><mml:mi mathvariant="normal">A</mml:mi></mml:mrow><mml:mi mathvariant="normal">c</mml:mi></mml:msub><mml:mo stretchy="true">/</mml:mo><mml:mspace width="0.25em"></mml:mspace><mml:mtext mathvariant="normal">breast</mml:mtext><mml:mspace width="0.25em"></mml:mspace><mml:mtext mathvariant="normal">area</mml:mtext><mml:mo>.</mml:mo></mml:math><graphic xlink:href="13058_2016_787_Article_Equg.gif" position="anchor"></graphic></alternatives></disp-formula></p><p>Equations to generate √DA, as if measured on a raw image, from DA measured on a processed image are provided in (Additional file <xref rid="MOESM7" ref-type="media">7</xref>: Information 1).</p><p>For the processed–SFM set (F2), comparing MD measured on the processed digital image with that on the earlier SFM, √breast area was 0.17 cm larger (95% CI: 0.06, 0.28) and √DA was 0.17 cm smaller (95% CI: 0.01, 0.33).</p></sec><sec id="Sec7"><title>Discussion</title><sec id="Sec8"><title>Findings</title><p>In the present study, we compared Cumulus-assessed MD measures (PMD, breast area and DA) on the same digital mammograms saved in processed and raw formats. Overall, we observed higher MD in the former image type, a difference that was not entirely consistent either in magnitude or direction across four readers for a given mammography system. Differences in MD assessed on raw and processed images were small for the CR system, but larger for direct digital systems. Differences between SFM and CR-digital images appeared to be small, although the latter were not time-matched comparisons. Readers had higher MD repeatability for SFM images than for raw or processed digital images. This may be because readers had more experience of reading from SFM images, or because density is more easily visualized in SFM images.</p></sec><sec id="Sec9"><title>Comparison and plausibility</title><p>Readers noted several appearance qualities of processed images that may affect the MD assessment, such as ‘thickened breast edge’ or ‘faded parenchyma’. Processing algorithms involve multiple steps designed to clarify the image, enhance suspected lesions and reduce noise—this noise may be dense tissue, therefore it has been hypothesized that density would be lower in processed images. However, this and similar studies generally found higher MD on processed images, particularly at lower density levels. Enhancement of light/dark transitions and accentuation of the breast edge may contribute to this increase. Differences in PMD were almost entirely driven by changes in the DA because breast area altered minimally. Our results are also consistent with those of Keller et al. [<xref ref-type="bibr" rid="CR10">10</xref>], and Martin et al. [<xref ref-type="bibr" rid="CR19">19</xref>], who reported that differences were highly reader dependent. Unsurprisingly, Vachon et al.’s results [<xref ref-type="bibr" rid="CR12">12</xref>], which comprised 14% of our raw–processed pairs, also found that PMD was overestimated in less dense breasts in processed compared with raw GE images. Studies that compared MD using the BIRADS classification did not find differences by image type [<xref ref-type="bibr" rid="CR20">20</xref>], but differences may be too small to be detected using a broad categorical classification.</p><p>Differences in MD assessment between SFM and Fuji CR were not assessed optimally, because they were based on films taken 2 years apart. While there was no breast area difference in the time-matched images, over this time interval the breast area increased indicating measurable age-related changes. The magnitude of this increase (0.17 cm √breast area) was consistent with the expected within-woman changes (0.16 cm over 2 years) found in a previous SFM-only longitudinal study [<xref ref-type="bibr" rid="CR21">21</xref>]. Similarly, the decline in DA was only slightly larger than would be expected from age-related changes (−0.13 cm √DA), suggesting that any differences due to image formats were small (at most 0.04 cm). However, similar studies comparing PMD in SFM and digital mammography reported that PMD was higher in SFM images than in raw or processed digital images [<xref ref-type="bibr" rid="CR22">22</xref>], including one in which the digital and the SFM were taken on the same day [<xref ref-type="bibr" rid="CR19">19</xref>]. In both studies the differences were larger than for the present study, possibly because they were comparing SFM with direct digital and not with CR as in the present study. Breast area was also higher in digital images taken on the same day as SFM images, indicating that lower PMD assessment may be a product of both underestimation of DA and overestimation of breast area in digital images compared with SFM images. Harvey [<xref ref-type="bibr" rid="CR22">22</xref>] hypothesized that more subcutaneous fat is included in digital measurements because the breast edge can be seen and delimited more precisely, but only PMD was reported in that study. In the present study, small differences between SFM and CR may reflect these closely related imaging technologies; CR systems are additions to SFM systems, using phosphor plates and a separate reader to create digital images, whereas the direct digital image is created at the point of image capture [<xref ref-type="bibr" rid="CR23">23</xref>]. Thus, CR images have lower spatial resolution and more image noise than direct digital images [<xref ref-type="bibr" rid="CR24">24</xref>]. The improved image quality in direct digital allows for more complex multi-functional processing algorithms, which may account for the larger raw–processed differences in direct digital images compared with CR images.</p></sec><sec id="Sec10"><title>Strengths and limitations</title><p>This is the first study to compare raw and processed images, using the same design and analytic approach, captured on several widely used mammography systems. Comparisons of MD across multiple systems are important because it is unlikely that all women in a study, or the same woman followed for several years, will be screened on the same mammography machine. Nevertheless, several design features would have improved the study; by including CC views alongside MLO for all images, and including other widely used mammography systems such as Siemens, and other CR systems. We were limited by the lack of information on manipulations performed by processing algorithms which are proprietary to manufacturers. Multiple readers are a further strength, being reflective of clinical and research settings—between-reader differences in raw–processed calibration highlight the need to recognize and quantify these differences where possible. Further, we used a reversible statistical method for processed–raw MD conversions; that is, neither raw nor processed MD is considered the error-free independent variable, which would not have been the case had a simple regression method been used. Finally, the women included in this study came from countries with a wide range of BC incidence rates, and thus the results should be generalizable to women across the BC risk spectrum.</p></sec><sec id="Sec11"><title>Relevance and implications</title><p>The potential impact of raw–processed differences in MD from direct-digital systems (3.3 percentage points) will depend on the application. When investigating MD as a predictor of BC risk, differences are unlikely to introduce substantial misclassification between very low density (<10%) and very high density (e.g. >50%) and would thus have a small impact on relative risk estimates. For investigations of determinants of MD or changes in MD, raw–processed differences are of a magnitude similar to 10 years of aging or the menopause-related PMD change (as assessed within ICMD) and depend greatly on the reader. Thus, in the screening or clinical setting when assessing MD change over time for the same woman, it is important that the same reader reads the woman’s repeat mammograms. If the calibration equations presented in this article are to be used in the screening or clinical settings, they will need to be validated, particularly for different readers. In studies comparing PMD across raw and processed image types, correcting for these differences is thus important and would ideally be made using reader-specific and system-specific calibrations. Even if all images are of the same type (raw or processed) it is necessary to calibrate between readers. Comparability of raw images between systems has not been assessed and difference in acquisition between systems may be present. The repeated finding across studies of large between-reader differences in MD, in addition to their time-intensive nature, again emphasizes the need for fully-automated methods of MD measurement. Four such fully automated quantitative methods were recently evaluated for BC risk prediction, alongside Cumulus [<xref ref-type="bibr" rid="CR7">7</xref>]. Although such methods eliminate between-reader variations in readings, many only work on a single image type (often raw digital images [<xref ref-type="bibr" rid="CR25">25</xref>]), but others can be applied across multiple types [<xref ref-type="bibr" rid="CR8">8</xref>, <xref ref-type="bibr" rid="CR26">26</xref>]. It is possible that there would be between-system differences in automated measures, particularly volumetric measures due to differences in breast positioning and therefore breast thickness [<xref ref-type="bibr" rid="CR27">27</xref>], but not all studies have found this [<xref ref-type="bibr" rid="CR28">28</xref>]. In the future, as further processing algorithms are developed, MD differences between raw and processed images are likely not only to persist but also to change. However, as digital storage becomes cheaper and faster, such problems may be overcome if raw images are systematically stored and MD is consistently measured on them. In a similar fashion, a consistent and fully-automated MD measurement tool could be applied to the raw image bank to provide MD data in an efficient and systematic manner.</p></sec></sec><sec id="Sec12"><title>Conclusion</title><p>Processed ‘for presentation’ direct digital mammograms have, on average, a higher Cumulus-assessed PMD and dense area compared with their corresponding raw ‘for processing’ images, whilst such differences were small for CR systems. Raw–processed differences in the direct digital systems depended on mammography system and to a large extent on reader, as did absolute density readings for a given image type. Controlling for these factors is necessary when comparing density readings across image types. For detection of small differences in density (e.g. within-woman changes), reader-specific processed to raw calibration, or restriction of comparisons to readings made by the same reader and on the same image type may be necessary.</p></sec></body><back><app-group><app id="App1"><sec id="Sec13"><title>Additional files</title><p><media position="anchor" xlink:href="13058_2016_787_MOESM1_ESM.doc" id="MOESM1"><label>Additional file 1:</label><caption><p>is <bold>Table S1</bold> presenting percent density, dense area and total breast area in raw–processed image pairs and in SFM–processed digital image pairs, by reader. (DOC 33 kb)</p></caption></media><media position="anchor" xlink:href="13058_2016_787_MOESM2_ESM.doc" id="MOESM2"><label>Additional file 2:</label><caption><p>is <bold>Table S2</bold> presenting mean MD measures of inter-reader repeats, by reader and image type. (DOC 29 kb)</p></caption></media><media position="anchor" xlink:href="13058_2016_787_MOESM3_ESM.doc" id="MOESM3"><label>Additional file 3:</label><caption><p>is <bold>Table S3</bold> presenting correlation of MD measures in inter-reader repeats. (DOC 29 kb)</p></caption></media><media position="anchor" xlink:href="13058_2016_787_MOESM4_ESM.doc" id="MOESM4"><label>Additional file 4:</label><caption><p>is <bold>Table S4</bold> presenting mean differences in MD measures between processed images and the corresponding raw digital image, by percent density and breast area categories. (DOC 30 kb)</p></caption></media><media position="anchor" xlink:href="13058_2016_787_MOESM5_ESM.docx" id="MOESM5"><label>Additional file 5:</label><caption><p>is <bold>Figure S1</bold> showing Bland–Altman plots for <italic>v</italic>MD measures, by mammography system and reader for: (<bold>A</bold>) percent mammographic density, (<bold>B</bold>) dense area and (<bold>C</bold>) breast area. <italic>Y</italic> axes to the same scale for comparisons. (DOCX 138 kb)</p></caption></media><media position="anchor" xlink:href="13058_2016_787_MOESM6_ESM.docx" id="MOESM6"><label>Additional file 6:</label><caption><p>is <bold>Figure S2</bold> showing Bland–Altman plots for within-system and within-reader standardized <italic>v</italic>DA measures. (DOCX 79 kb)</p></caption></media><media position="anchor" xlink:href="13058_2016_787_MOESM7_ESM.doc" id="MOESM7"><label>Additional file 7:</label><caption><p>is <bold>Information 1</bold> showing calibration equations for the conversion of raw <italic>v</italic>DA to processed <italic>v</italic>DA, and Information 2 showing calibration equations for the conversion of processed <italic>v</italic>DA to raw <italic>v</italic>DA. (DOC 41 kb)</p></caption></media></p></sec></app></app-group><glossary><title>Abbreviations</title><def-list><def-item><term>BC</term><def><p>Breast cancer</p></def></def-item><def-item><term>BMI</term><def><p>Body mass index</p></def></def-item><def-item><term>CC</term><def><p>Craniocaudal</p></def></def-item><def-item><term>CI</term><def><p>Confidence interval</p></def></def-item><def-item><term>CR</term><def><p>Computed radiography</p></def></def-item><def-item><term>DA</term><def><p>Dense area</p></def></def-item><def-item><term>GE</term><def><p>General Electric</p></def></def-item><def-item><term>ICC</term><def><p>Intra-class correlation coefficient</p></def></def-item><def-item><term>ICMD</term><def><p>International Consortium on Mammographic Density</p></def></def-item><def-item><term>MD</term><def><p>Mammographic density</p></def></def-item><def-item><term>MLO</term><def><p>Mediolateral oblique</p></def></def-item><def-item><term>PMD</term><def><p>Percent mammographic density</p></def></def-item><def-item><term>SD</term><def><p>Standard deviation</p></def></def-item><def-item><term>SFM</term><def><p>Screen-film mammography</p></def></def-item></def-list></glossary><ack><title>Acknowledgements</title><p>The authors would like to thank BreastScreen Victoria and Dr Ralph Highnam for the facilitation of image acquisition. Previous studies were supported by: Australia—Australian National Breast Cancer Foundation (to JSt), MCCS by VicHealth, Cancer Council Victoria and Australian NHMRC grants 209057, 251553 and 504711, and cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database; Canada—National Cancer Institute of Canada (to NFB); Chile—Fondecyt 11100238 (to MLG), 1120326, 1130277 and 3130532, World Cancer Research Fund 2010/245, Ellison Medical Foundation Grant (to AP); Iran—Isfahan University of Medical Sciences, and assistance from Dr Vida Razavi and Dr Shamila Razavi; Israel—The Israel Cancer Association; Republic of Korea—Asan Medical Center, Seoul, Republic of Korea, Grant No. 2010–0811; Malaysia—Sime Darby LPGA Tournament, Ministry of Education University Malaya High Impact Research Grant UM.C/HIR/MOHE/06 and University Malaya Research Grant (UMRG Grant No.: RP046B-15HTM); Mexico—Ministry of Education of Mexico and ISSSTE’s Medical Directorate staff and regional office in Jalisco for technical and administrative support, National Council of Science and Technology (Mexico) and the American Institute for Cancer Research (10A035); the Netherlands—EPIC-NL-Europe against Cancer Programme of the European Commission (SANCO), Dutch Ministry of Health, Dutch Cancer Society, ZonMW the Netherlands Organisation for Health Research and Development, and the World Cancer Research Fund (WCRF); Poland—Polish–Norwegian Research Programme (PNRF–243–AI–1/07); Singapore—Clinician Scientist Award from National Medical Research Council and National University Cancer Institute Singapore (NCIS) Centre grant programme from National Medical Research Council; South Africa—Pink Drive; Spain—Spain’s Health Research Fund (Fondo de Investigacion Santiaria) PI060386 and PS09/0790, and Spanish Federation of Breast Cancer Patients (FECMA) EPY1169-10; Turkey—Roche Mustahzarlari San. A.S., Istanbul, Turkey; UK—EPSRC and EP/K020439/1 (JHH), Breast Cancer Campaign (2007MayPR23), Cancer Research UK (G186/11; C405/A14565), Da Costa Foundation; USA—National Cancer Institute R01CA85265, R37 CA54281, R01 CA97396, P50 CA116201, R01 CA177150 and R01 CA140286, Cancer Center Support Grant CA15083, CA131332, CA124865, UM1 CA186107 and UM1 CA176726 and the Susan G. Komen Foundation.</p><sec id="FPar1"><title>Funding</title><p>This work was supported by the US National Cancer Institute at the National Institutes of Health (R03CA167771) and by the International Agency for Research on Cancer.</p></sec><sec id="FPar2"><title>Availability of data and materials</title><p>The data that support the findings of this study are available from the ICMD. Upon agreement of ICMD collaborators, these data can be accessed and analysed at IARC.</p></sec><sec id="FPar3"><title>Authors' contributions</title><p>ABur, GB, JSt, RMT, JHe, CV, VO, AP, MLG, CS, JHH, CD, JSc, MEA, KB, AK, GGG, JHo, BP-G, MP, S-HT, SM, NAMT, ML, RL-R, MR, IR, AAF, GU, SQ, HM, EL, RS, MS, JWL, JK, DS, RK, MH, HM, K-SC, CN, SV, RN, CHvG, JOPW, BP, ABuk, SA, SV, SM, AMC, LL, GM, MJY, NFB, Id-S-S and VAM made substantial contributions to acquisition of data, and critical review of the manuscript. ABur and VAM conceived of the study, coordinated the data acquisition and analysis, and drafted the manuscript. VAM, Id-S-S and NFB assessed MD on the acquired images. GB participated in planning the statistical methods. ABur, GB, JSt, RMT, NFB, Id-S-S and VAM made substantial contributions to conception and design, analysis and interpretation of data. All authors read and approved the final manuscript.</p></sec><sec id="FPar4"><title>Competing interests</title><p>ML received a non-restricted investigator-initiated grant from AstraZeneca and minor support from Swiss Re.</p></sec><sec id="FPar5"><title>Consent for publication</title><p>Not applicable.</p></sec><sec id="FPar6"><title>Ethics approval and consent to participate</title><p>Ethics approvals were obtained from IARC (IEC 12–34 for the ICMD) for the consortium and from each contributing study from local institutions. 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