{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:37:38Z","timestamp":1782434258496,"version":"3.54.5"},"reference-count":19,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008050","name":"Fundaci\u00f3 Privada CELLEX","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100008050","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2020,1,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>Delineation of organs at risk is a time-consuming task. This study evaluates the benefits of using single-subject atlas-based automatic segmentation of organs at risk in patients with breast cancer treated in prone position, with 2 different criteria for choosing the atlas subject. Together with laterality (left\/right), the criteria used were either (1) breast volume or (2) body mass index and breast cup size.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>An atlas supporting different selection criteria for automatic segmentation was generated from contours drawn by a senior radiation oncologist (RO_A). Atlas organs at risk included heart, left anterior descending artery, and right coronary artery. Manual contours drawn by RO_A and automatic segmentation contours of organs at risk and breast clinical target volume were created for 27 nonatlas patients. A second radiation oncologist (RO_B) manually contoured (M_B) the breast clinical target volume and the heart. Contouring times were recorded and the reliability of the automatic segmentation was assessed in the context of 3-D planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Accounting for body mass index and breast cup size improved automatic segmentation results compared to breast volume-based sampling, especially for the heart (mean similarity indexes &gt;0.9 for automatic segmentation organs at risk and clinical target volume after RO_A editing). Mean similarity indexes for the left anterior descending artery and the right coronary artery edited by RO_A expanded by 1 cm were \u22650.8. Using automatic segmentation reduced contouring time by 40%. For each parameter analyzed (eg, D\n                      <jats:sub>2%<\/jats:sub>\n                      ), the difference in dose, averaged over all patients, between automatic segmentation structures edited by RO_A and the same structure manually drawn by RO_A was &lt;1.5% of the prescribed dose. The mean heart dose was reliable for the unedited heart segmentation, and for right-sided treatments, automatic segmentation was adequate for treatment planning with 3-D conformal tangential fields.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Automatic segmentation for prone breast radiotherapy stratified by body mass index and breast cup size improved segmentation accuracy for the heart and coronary vessels compared to breast volume sampling. A significant reduction in contouring time can be achieved by using automatic segmentation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/1533033820920624","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T06:27:45Z","timestamp":1587450465000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Atlas Sampling for Prone Breast Automatic Segmentation of Organs at Risk: The Importance of Patients\u2019 Body Mass Index and Breast Cup Size for an Optimized Contouring of the Heart and the Coronary Vessels"],"prefix":"10.1177","volume":"19","author":[{"given":"Xinzhuo","family":"Wang","sequence":"first","affiliation":[{"name":"Division of Radiation Oncology, Tianjin Union Medicine Center, China"},{"name":"Division of Radiation Oncology, Geneva University Hospital, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond","family":"Miralbell","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology, Geneva University Hospital, Switzerland"},{"name":"Institut Oncol\u00f2gic Teknon, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Odile","family":"Fargier-Bochaton","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology, Geneva University Hospital, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shelley","family":"Bulling","sequence":"additional","affiliation":[{"name":"Centre d\u2019Oncologie des Eaux-Vives, Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean Paul","family":"Vall\u00e9e","sequence":"additional","affiliation":[{"name":"Radiology division, Diagnostic department, Geneva University Hospital and University of Geneva, Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanna","family":"Dipasquale","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology, Geneva University Hospital, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2020,4,21]]},"reference":[{"key":"bibr1-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.09.005"},{"key":"bibr2-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.04.041"},{"key":"bibr3-1533033820920624","doi-asserted-by":"publisher","DOI":"10.3109\/0284186X.2014.930170"},{"key":"bibr4-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2011.08.043"},{"key":"bibr5-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2008.07.001"},{"key":"bibr6-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2008.10.034"},{"key":"bibr7-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/57\/2\/473"},{"key":"bibr8-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.10.058"},{"key":"bibr9-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2010.05.014"},{"key":"bibr10-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/50\/8\/010"},{"key":"bibr11-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1186\/1748-717X-7-160"},{"key":"bibr12-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2013.06.025"},{"key":"bibr13-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2018.07.013"},{"key":"bibr14-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1209825"},{"key":"bibr15-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2014.11.018"},{"key":"bibr16-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.09.030"},{"key":"bibr17-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2014.11.038"},{"key":"bibr18-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20120643"},{"key":"bibr19-1533033820920624","doi-asserted-by":"publisher","DOI":"10.1118\/1.4871620"}],"container-title":["Technology in Cancer Research &amp; 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