Pain perception and detailed visual pain mapping in breast cancer survivors.
Identifieur interne : 005768 ( Main/Merge ); précédent : 005767; suivant : 005769Pain perception and detailed visual pain mapping in breast cancer survivors.
Auteurs : Sebastian M. Jud [Allemagne] ; Peter A. Fasching ; Christian Maihöfner ; Katharina Heusinger ; Christian R. Loehberg ; Reinhard Hatko ; Claudia Rauh ; Hiba Bani ; Michael P. Lux ; Matthias W. Beckmann ; Mayada R. BaniSource :
- Breast cancer research and treatment [ 1573-7217 ] ; 2010.
Descripteurs français
- KwdFr :
- Adulte, Adulte d'âge moyen, Algorithmes, Douleur (anatomopathologie), Douleur (diagnostic), Douleur thoracique (diagnostic), Enquêtes et questionnaires, Femelle, Humains, Logiciel, Mastectomie (effets indésirables), Mesure de la douleur, Modèles statistiques, Perception, Période postopératoire, Région mammaire (anatomopathologie), Sujet âgé, Tumeurs du sein (), Études transversales.
- MESH :
- anatomopathologie : Douleur, Région mammaire.
- diagnostic : Douleur, Douleur thoracique.
- effets indésirables : Mastectomie.
- Adulte, Adulte d'âge moyen, Algorithmes, Enquêtes et questionnaires, Femelle, Humains, Logiciel, Mesure de la douleur, Modèles statistiques, Perception, Période postopératoire, Sujet âgé, Tumeurs du sein, Études transversales.
English descriptors
- KwdEn :
- Adult, Aged, Algorithms, Breast (pathology), Breast Neoplasms (complications), Breast Neoplasms (surgery), Chest Pain (diagnosis), Cross-Sectional Studies, Female, Humans, Mastectomy (adverse effects), Middle Aged, Models, Statistical, Pain (diagnosis), Pain (pathology), Pain Measurement, Perception, Postoperative Period, Software, Surveys and Questionnaires.
- MESH :
- adverse effects : Mastectomy.
- complications : Breast Neoplasms.
- diagnosis : Chest Pain, Pain.
- pathology : Breast, Pain.
- surgery : Breast Neoplasms.
- Adult, Aged, Algorithms, Cross-Sectional Studies, Female, Humans, Middle Aged, Models, Statistical, Pain Measurement, Perception, Postoperative Period, Software, Surveys and Questionnaires.
Abstract
Chronic pain and neural irritation after breast surgery and radiation are still relevant sequelae of the treatment. Pain quantification and localization in patient groups are difficult to standardize. In order to quantify and localize pain in a group of breast cancer patients, a Java-based program was developed to visualize the frequency of pain in "pain maps." A questionnaire with structured questions on the perception of pain included pictograms of a body to mark possible pain areas. A group of 343 breast cancer survivors completed the questionnaires. The image information was digitalized and processed using a Java applet. Gray-scale summation pictures with numbers from "0," indicating black (100% pain), to "255," indicating white (0% pain), were generated. The visualization of pain by creating pain maps revealed the location of pain in breast cancer survivors on pictograms of the body. Analyzing the total number of pixels, in which pain was stated, made it possible to compare pain areas in several subgroups, showing that patients after mastectomy versus breast-conserving therapy (3,011 vs. 2,224 pixels), and patients with lymphedema versus patients without lymphedema (3,010 vs. 2,239 pixels), have larger pain areas. This study presents a method of visualizing pain areas and assigning them to a pictogram of the body in a sample of breast cancer patients. The method is easy to use and could help generate pain maps in several types of disease.
DOI: 10.1007/s10549-009-0485-z
PubMed: 19641989
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pubmed:19641989Le document en format XML
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Jud</name><affiliation wicri:level="3"><nlm:affiliation>Department of Gynecology and Obstetrics, University Breast Center for Franconia, Erlangen University Hospital, Erlangen, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Department of Gynecology and Obstetrics, University Breast Center for Franconia, Erlangen University Hospital, Erlangen</wicri:regionArea><placeName><settlement type="city">Erlangen</settlement><region type="land" nuts="1">Bavière</region><region type="district" nuts="2">District de Moyenne-Franconie</region></placeName></affiliation></author><author><name sortKey="Fasching, Peter A" sort="Fasching, Peter A" uniqKey="Fasching P" first="Peter A" last="Fasching">Peter A. Fasching</name></author><author><name sortKey="Maihofner, Christian" sort="Maihofner, Christian" uniqKey="Maihofner C" first="Christian" last="Maihöfner">Christian Maihöfner</name></author><author><name sortKey="Heusinger, Katharina" sort="Heusinger, Katharina" uniqKey="Heusinger K" first="Katharina" last="Heusinger">Katharina Heusinger</name></author><author><name sortKey="Loehberg, Christian R" sort="Loehberg, Christian R" uniqKey="Loehberg C" first="Christian R" last="Loehberg">Christian R. Loehberg</name></author><author><name sortKey="Hatko, Reinhard" sort="Hatko, Reinhard" uniqKey="Hatko R" first="Reinhard" last="Hatko">Reinhard Hatko</name></author><author><name sortKey="Rauh, Claudia" sort="Rauh, Claudia" uniqKey="Rauh C" first="Claudia" last="Rauh">Claudia Rauh</name></author><author><name sortKey="Bani, Hiba" sort="Bani, Hiba" uniqKey="Bani H" first="Hiba" last="Bani">Hiba Bani</name></author><author><name sortKey="Lux, Michael P" sort="Lux, Michael P" uniqKey="Lux M" first="Michael P" last="Lux">Michael P. Lux</name></author><author><name sortKey="Beckmann, Matthias W" sort="Beckmann, Matthias W" uniqKey="Beckmann M" first="Matthias W" last="Beckmann">Matthias W. Beckmann</name></author><author><name sortKey="Bani, Mayada R" sort="Bani, Mayada R" uniqKey="Bani M" first="Mayada R" last="Bani">Mayada R. 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Jud</name><affiliation wicri:level="3"><nlm:affiliation>Department of Gynecology and Obstetrics, University Breast Center for Franconia, Erlangen University Hospital, Erlangen, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Department of Gynecology and Obstetrics, University Breast Center for Franconia, Erlangen University Hospital, Erlangen</wicri:regionArea><placeName><settlement type="city">Erlangen</settlement><region type="land" nuts="1">Bavière</region><region type="district" nuts="2">District de Moyenne-Franconie</region></placeName></affiliation></author><author><name sortKey="Fasching, Peter A" sort="Fasching, Peter A" uniqKey="Fasching P" first="Peter A" last="Fasching">Peter A. Fasching</name></author><author><name sortKey="Maihofner, Christian" sort="Maihofner, Christian" uniqKey="Maihofner C" first="Christian" last="Maihöfner">Christian Maihöfner</name></author><author><name sortKey="Heusinger, Katharina" sort="Heusinger, Katharina" uniqKey="Heusinger K" first="Katharina" last="Heusinger">Katharina Heusinger</name></author><author><name sortKey="Loehberg, Christian R" sort="Loehberg, Christian R" uniqKey="Loehberg C" first="Christian R" last="Loehberg">Christian R. Loehberg</name></author><author><name sortKey="Hatko, Reinhard" sort="Hatko, Reinhard" uniqKey="Hatko R" first="Reinhard" last="Hatko">Reinhard Hatko</name></author><author><name sortKey="Rauh, Claudia" sort="Rauh, Claudia" uniqKey="Rauh C" first="Claudia" last="Rauh">Claudia Rauh</name></author><author><name sortKey="Bani, Hiba" sort="Bani, Hiba" uniqKey="Bani H" first="Hiba" last="Bani">Hiba Bani</name></author><author><name sortKey="Lux, Michael P" sort="Lux, Michael P" uniqKey="Lux M" first="Michael P" last="Lux">Michael P. Lux</name></author><author><name sortKey="Beckmann, Matthias W" sort="Beckmann, Matthias W" uniqKey="Beckmann M" first="Matthias W" last="Beckmann">Matthias W. Beckmann</name></author><author><name sortKey="Bani, Mayada R" sort="Bani, Mayada R" uniqKey="Bani M" first="Mayada R" last="Bani">Mayada R. Bani</name></author></analytic><series><title level="j">Breast cancer research and treatment</title><idno type="eISSN">1573-7217</idno><imprint><date when="2010" type="published">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term><term>Aged</term><term>Algorithms</term><term>Breast (pathology)</term><term>Breast Neoplasms (complications)</term><term>Breast Neoplasms (surgery)</term><term>Chest Pain (diagnosis)</term><term>Cross-Sectional Studies</term><term>Female</term><term>Humans</term><term>Mastectomy (adverse effects)</term><term>Middle Aged</term><term>Models, Statistical</term><term>Pain (diagnosis)</term><term>Pain (pathology)</term><term>Pain Measurement</term><term>Perception</term><term>Postoperative Period</term><term>Software</term><term>Surveys and Questionnaires</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Algorithmes</term><term>Douleur (anatomopathologie)</term><term>Douleur (diagnostic)</term><term>Douleur thoracique (diagnostic)</term><term>Enquêtes et questionnaires</term><term>Femelle</term><term>Humains</term><term>Logiciel</term><term>Mastectomie (effets indésirables)</term><term>Mesure de la douleur</term><term>Modèles statistiques</term><term>Perception</term><term>Période postopératoire</term><term>Région mammaire (anatomopathologie)</term><term>Sujet âgé</term><term>Tumeurs du sein ()</term><term>Études transversales</term></keywords><keywords scheme="MESH" qualifier="adverse effects" xml:lang="en"><term>Mastectomy</term></keywords><keywords scheme="MESH" qualifier="anatomopathologie" xml:lang="fr"><term>Douleur</term><term>Région mammaire</term></keywords><keywords scheme="MESH" qualifier="complications" xml:lang="en"><term>Breast Neoplasms</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Chest Pain</term><term>Pain</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Douleur</term><term>Douleur thoracique</term></keywords><keywords scheme="MESH" qualifier="effets indésirables" xml:lang="fr"><term>Mastectomie</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Breast</term><term>Pain</term></keywords><keywords scheme="MESH" qualifier="surgery" xml:lang="en"><term>Breast Neoplasms</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Aged</term><term>Algorithms</term><term>Cross-Sectional Studies</term><term>Female</term><term>Humans</term><term>Middle Aged</term><term>Models, Statistical</term><term>Pain Measurement</term><term>Perception</term><term>Postoperative Period</term><term>Software</term><term>Surveys and Questionnaires</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Algorithmes</term><term>Enquêtes et questionnaires</term><term>Femelle</term><term>Humains</term><term>Logiciel</term><term>Mesure de la douleur</term><term>Modèles statistiques</term><term>Perception</term><term>Période postopératoire</term><term>Sujet âgé</term><term>Tumeurs du sein</term><term>Études transversales</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Chronic pain and neural irritation after breast surgery and radiation are still relevant sequelae of the treatment. Pain quantification and localization in patient groups are difficult to standardize. In order to quantify and localize pain in a group of breast cancer patients, a Java-based program was developed to visualize the frequency of pain in "pain maps." A questionnaire with structured questions on the perception of pain included pictograms of a body to mark possible pain areas. A group of 343 breast cancer survivors completed the questionnaires. The image information was digitalized and processed using a Java applet. Gray-scale summation pictures with numbers from "0," indicating black (100% pain), to "255," indicating white (0% pain), were generated. The visualization of pain by creating pain maps revealed the location of pain in breast cancer survivors on pictograms of the body. Analyzing the total number of pixels, in which pain was stated, made it possible to compare pain areas in several subgroups, showing that patients after mastectomy versus breast-conserving therapy (3,011 vs. 2,224 pixels), and patients with lymphedema versus patients without lymphedema (3,010 vs. 2,239 pixels), have larger pain areas. This study presents a method of visualizing pain areas and assigning them to a pictogram of the body in a sample of breast cancer patients. The method is easy to use and could help generate pain maps in several types of disease.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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