Factor analysis-based approach for early uptake automatic quantification of breast cancer by 18F-FDG PET images sequence
Identifieur interne : 000124 ( Hal/Curation ); précédent : 000123; suivant : 000125Factor analysis-based approach for early uptake automatic quantification of breast cancer by 18F-FDG PET images sequence
Auteurs : Ines Ketata [Tunisie] ; Lamia Sallemi [Tunisie] ; Frederic Morain-Nicolier [France] ; Mohamed Ben Slima [Tunisie] ; Alexandre Cochet [France] ; Khalil Chtourou [Tunisie] ; Su Ruan [France] ; Ahmed Ben Hamida [Tunisie]Source :
- Biomedical Signal Processing and Control [ 1746-8094 ] ; 2014.
Abstract
Factor Analysis of Medical Image Sequences (FAMIS) is recognized as one pioneer successfully used approach for analyzing especially dynamic images' sequence for estimating kinetics and associated compartments having a physiological meaning. Some studies tried to extend the exploring of this approach to analyze Positron Emission Tomography (PET) image modality for dynamic sequences. PET images with 18F-fluorodesoxyglucose (18F-FDG) is the gold standard for in vivo, evaluation of tumor glucose metabolism and is widely used in clinical oncology. In this paper, a novel approach is proposed to obtain an automated quantification method for early accumulation of 18F-FDG tracer in order to explore breast cancer, by applying FAMIS tool on dynamic first pass 18F-FDG PET dynamic sequences. This approach starts by an automated identification of a tumor Region of Interest (ROI) from PET dynamic images' sequence. Then, a FAMIS approach is applied to separate two compartments: one compartment is associated to the vascular and a second one is associated to the purely tumor compartment. The latter allows the evaluation of the temporal evolution of the glucose tracer metabolism and therefore for pursuing cancer characterization. A new empiric parameter KFPQ (First Pass Quantification), computed from the evolution of the 18F-FDG radiotracer accumulation using the first 11 min PET early images, is proposed. This parameter is found to be correlated to standardized uptake value maximal index (SUVmax) metabolism tumor. The proposed framework is tested using image sequences' database for 25 different pathology cases, which is considered as largely sufficient by the clinical team. Among clinicians' experience, using a large dataset permits the possibility to obtain accurate information and precise early diagnosis. Pearson correlation coefficient is computed to evaluate as well as to analyze the relationship between the proposed empiric parameter KFPQ and glucose tracer metabolism SUVmax for the overall pathology cases. KFPQ is successfully evaluated by the dynamic first-pass 18F-FDG PET image sequences for exploring early breast cancer diagnosis. Quantitative evaluations, as discussed and validated by clinicians, confirmed the efficiency of the modeling and the usefulness of the new empiric parameter KFPQ to predict tumor glucose metabolism for early uptake. This can be considered as a significant indication for quantification as well as evaluation of early relapse and disease progression during the therapy.
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G.E, (ENIS), B.P. 1173, 3038 Sfax</addrLine><country key="TN"></country></address><ref type="url">http://www.enis.rnu.tn/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>Tunisie</country></affiliation></author></analytic><series><title level="j">Biomedical Signal Processing and Control</title><idno type="ISSN">1746-8094</idno><imprint><date type="datePub">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Factor Analysis of Medical Image Sequences (FAMIS) is recognized as one pioneer successfully used approach for analyzing especially dynamic images' sequence for estimating kinetics and associated compartments having a physiological meaning. Some studies tried to extend the exploring of this approach to analyze Positron Emission Tomography (PET) image modality for dynamic sequences. PET images with 18F-fluorodesoxyglucose (18F-FDG) is the gold standard for in vivo, evaluation of tumor glucose metabolism and is widely used in clinical oncology. In this paper, a novel approach is proposed to obtain an automated quantification method for early accumulation of 18F-FDG tracer in order to explore breast cancer, by applying FAMIS tool on dynamic first pass 18F-FDG PET dynamic sequences. This approach starts by an automated identification of a tumor Region of Interest (ROI) from PET dynamic images' sequence. Then, a FAMIS approach is applied to separate two compartments: one compartment is associated to the vascular and a second one is associated to the purely tumor compartment. The latter allows the evaluation of the temporal evolution of the glucose tracer metabolism and therefore for pursuing cancer characterization. A new empiric parameter KFPQ (First Pass Quantification), computed from the evolution of the 18F-FDG radiotracer accumulation using the first 11 min PET early images, is proposed. This parameter is found to be correlated to standardized uptake value maximal index (SUVmax) metabolism tumor. The proposed framework is tested using image sequences' database for 25 different pathology cases, which is considered as largely sufficient by the clinical team. Among clinicians' experience, using a large dataset permits the possibility to obtain accurate information and precise early diagnosis. Pearson correlation coefficient is computed to evaluate as well as to analyze the relationship between the proposed empiric parameter KFPQ and glucose tracer metabolism SUVmax for the overall pathology cases. KFPQ is successfully evaluated by the dynamic first-pass 18F-FDG PET image sequences for exploring early breast cancer diagnosis. Quantitative evaluations, as discussed and validated by clinicians, confirmed the efficiency of the modeling and the usefulness of the new empiric parameter KFPQ to predict tumor glucose metabolism for early uptake. This can be considered as a significant indication for quantification as well as evaluation of early relapse and disease progression during the therapy.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Factor analysis-based approach for early uptake automatic quantification of breast cancer by 18F-FDG PET images sequence</title><author role="aut"><persName><forename type="first">Ines</forename><surname>Ketata</surname></persName><email></email><idno type="halauthor">970497</idno><affiliation ref="#struct-241555"></affiliation><affiliation ref="#struct-21189"></affiliation></author><author role="aut"><persName><forename type="first">Lamia</forename><surname>Sallemi</surname></persName><email></email><idno type="halauthor">970498</idno><affiliation ref="#struct-303396"></affiliation></author><author role="aut"><persName><forename type="first">Frederic</forename><surname>Morain-Nicolier</surname></persName><email></email><idno type="halauthor">696765</idno><affiliation ref="#struct-21189"></affiliation></author><author role="aut"><persName><forename type="first">Mohamed</forename><surname>Ben Slima</surname></persName><email></email><idno type="halauthor">970499</idno><affiliation ref="#struct-303396"></affiliation></author><author role="aut"><persName><forename type="first">Alexandre</forename><surname>Cochet</surname></persName><email>alexandre.cochet@u-bourgogne.fr</email><idno type="halauthor">848107</idno><affiliation ref="#struct-22594"></affiliation></author><author role="aut"><persName><forename type="first">Khalil</forename><surname>Chtourou</surname></persName><email></email><idno type="halauthor">970500</idno><affiliation ref="#struct-303396"></affiliation></author><author role="aut"><persName><forename type="first">Su</forename><surname>Ruan</surname></persName><email></email><idno type="halauthor">114494</idno><affiliation ref="#struct-23832"></affiliation><affiliation ref="#struct-110426"></affiliation></author><author role="aut"><persName><forename type="first">Ahmed</forename><surname>Ben Hamida</surname></persName><email></email><idno type="halauthor">240917</idno><affiliation ref="#struct-184936"></affiliation></author><editor role="depositor"><persName><forename>Alexandre</forename><surname>Cochet</surname></persName><email>alexandre.cochet@u-bourgogne.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2014-01-14 16:51:03</date><date type="whenModified">2015-05-20 01:03:46</date><date type="whenReleased">2014-01-14 16:51:03</date><date type="whenProduced">2014</date></edition><respStmt><resp>contributor</resp><name key="181634"><persName><forename>Alexandre</forename><surname>Cochet</surname></persName><email>alexandre.cochet@u-bourgogne.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-00930935</idno><idno type="halUri">https://hal.archives-ouvertes.fr/hal-00930935</idno><idno type="halBibtex">ketata:hal-00930935</idno><idno type="halRefHtml">Biomedical Signal Processing and Control, Elsevier, 2014, 9, pp.19-31</idno><idno type="halRef">Biomedical Signal Processing and Control, Elsevier, 2014, 9, pp.19-31</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="UNIV-LEHAVRE">Université du Havre</idno><idno type="stamp" n="UNIV-ROUEN">Université de Rouen</idno><idno type="stamp" n="ENSAM">Ecole Nationale Supérieure d'Arts et Métiers</idno><idno type="stamp" n="LITIS">Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes</idno><idno type="stamp" n="UNIV-BOURGOGNE">Université de Bourgogne</idno><idno type="stamp" n="LE2I" p="UNIV-BOURGOGNE">Laboratoire Electronique, Informatique et Image</idno><idno type="stamp" n="COMUE-NORMANDIE">Normandie Université</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Factor analysis-based approach for early uptake automatic quantification of breast cancer by 18F-FDG PET images sequence</title><author role="aut"><persName><forename type="first">Ines</forename><surname>Ketata</surname></persName><idno type="halAuthorId">970497</idno><affiliation ref="#struct-241555"></affiliation><affiliation ref="#struct-21189"></affiliation></author><author role="aut"><persName><forename type="first">Lamia</forename><surname>Sallemi</surname></persName><idno type="halAuthorId">970498</idno><affiliation ref="#struct-303396"></affiliation></author><author role="aut"><persName><forename type="first">Frederic</forename><surname>Morain-Nicolier</surname></persName><idno type="halAuthorId">696765</idno><affiliation ref="#struct-21189"></affiliation></author><author role="aut"><persName><forename type="first">Mohamed</forename><surname>Ben Slima</surname></persName><idno type="halAuthorId">970499</idno><affiliation ref="#struct-303396"></affiliation></author><author role="aut"><persName><forename type="first">Alexandre</forename><surname>Cochet</surname></persName><email>alexandre.cochet@u-bourgogne.fr</email><idno type="halAuthorId">848107</idno><affiliation ref="#struct-22594"></affiliation></author><author role="aut"><persName><forename type="first">Khalil</forename><surname>Chtourou</surname></persName><idno type="halAuthorId">970500</idno><affiliation ref="#struct-303396"></affiliation></author><author role="aut"><persName><forename type="first">Su</forename><surname>Ruan</surname></persName><idno type="halAuthorId">114494</idno><affiliation ref="#struct-23832"></affiliation><affiliation ref="#struct-110426"></affiliation></author><author role="aut"><persName><forename type="first">Ahmed</forename><surname>Ben Hamida</surname></persName><idno type="halAuthorId">240917</idno><affiliation ref="#struct-184936"></affiliation></author></analytic><monogr><idno type="halJournalId" status="VALID">11194</idno><idno type="issn">1746-8094</idno><title level="j">Biomedical Signal Processing and Control</title><imprint><publisher>Elsevier</publisher><biblScope unit="volume">9</biblScope><biblScope unit="pp">19-31</biblScope><date type="datePub">2014</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halDomain" n="sdv.ib.mn">Life Sciences [q-bio]/Bioengineering/Nuclear medicine</classCode><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">Factor Analysis of Medical Image Sequences (FAMIS) is recognized as one pioneer successfully used approach for analyzing especially dynamic images' sequence for estimating kinetics and associated compartments having a physiological meaning. Some studies tried to extend the exploring of this approach to analyze Positron Emission Tomography (PET) image modality for dynamic sequences. PET images with 18F-fluorodesoxyglucose (18F-FDG) is the gold standard for in vivo, evaluation of tumor glucose metabolism and is widely used in clinical oncology. In this paper, a novel approach is proposed to obtain an automated quantification method for early accumulation of 18F-FDG tracer in order to explore breast cancer, by applying FAMIS tool on dynamic first pass 18F-FDG PET dynamic sequences. This approach starts by an automated identification of a tumor Region of Interest (ROI) from PET dynamic images' sequence. Then, a FAMIS approach is applied to separate two compartments: one compartment is associated to the vascular and a second one is associated to the purely tumor compartment. The latter allows the evaluation of the temporal evolution of the glucose tracer metabolism and therefore for pursuing cancer characterization. A new empiric parameter KFPQ (First Pass Quantification), computed from the evolution of the 18F-FDG radiotracer accumulation using the first 11 min PET early images, is proposed. This parameter is found to be correlated to standardized uptake value maximal index (SUVmax) metabolism tumor. The proposed framework is tested using image sequences' database for 25 different pathology cases, which is considered as largely sufficient by the clinical team. Among clinicians' experience, using a large dataset permits the possibility to obtain accurate information and precise early diagnosis. Pearson correlation coefficient is computed to evaluate as well as to analyze the relationship between the proposed empiric parameter KFPQ and glucose tracer metabolism SUVmax for the overall pathology cases. KFPQ is successfully evaluated by the dynamic first-pass 18F-FDG PET image sequences for exploring early breast cancer diagnosis. Quantitative evaluations, as discussed and validated by clinicians, confirmed the efficiency of the modeling and the usefulness of the new empiric parameter KFPQ to predict tumor glucose metabolism for early uptake. This can be considered as a significant indication for quantification as well as evaluation of early relapse and disease progression during the therapy.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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