{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:46:37Z","timestamp":1792082797813,"version":"4.4.0"},"reference-count":24,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP24K10853"],"award-info":[{"award-number":["JP24K10853"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Automatic contouring can reduce the time required for delineating organs at risk (OARs) in brachytherapy planning and minimize interobserver variability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to develop and evaluate a deep learning\u2010based automatic contouring model for OARs in three\u2010dimensional image\u2010guided brachytherapy (3D\u2010IGBT) for cervical and endometrial cancer, including cases with interstitial needles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The dataset comprised 100 patients (140 cases) with cervical or endometrial cancer who underwent 3D\u2010IGBT. Interstitial needles were used in 74 cases. The nnU\u2010Net model was trained (80 patients, 80 cases) and tested (20 patients, 60 cases). The OARs considered were the bladder, small bowel, rectum, and sigmoid. Ground truth (GT) contours were manually delineated by radiation oncologists and medical physicists. Evaluation included measuring inference time and assessing geometric agreement using dice similarity coefficient (DSC), surface DSC (sDSC), Hausdorff distance (HD), and 95th percentile HD (95HD). These metrics were also calculated for a combined structure of the rectum and sigmoid (Rec+Sig). Furthermore, D\n                      <jats:sub>2cc<\/jats:sub>\n                      was calculated based on both the GT and predicted contours using the clinical dose distribution, and the difference between them (\u0394D\n                      <jats:sub>2cc<\/jats:sub>\n                      ) was evaluated. Differences in accuracy with or without interstitial needles were compared using Welch's\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test (significance level:\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Mean processing time was 30.3 s per case. Mean DSC values for the bladder, small bowel, rectum, sigmoid, and Rec+Sig were 0.96, 0.79, 0.83, 0.76, and 0.87, respectively. Mean 95HD values (mm) were 4.01, 18.8, 13.6, 25.5, and 17.8, respectively; \u0394D\n                      <jats:sub>2cc<\/jats:sub>\n                      values (Gy) were 0.17, 0.53, 0.014, \u22120.073, and \u22120.045, respectively. No significant accuracy differences related to interstitial needles were observed for any of the OARs.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed deep learning model demonstrated potential for application in cases involving interstitial needles and may contribute to improving the efficiency of the treatment planning workflow.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70570","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T04:36:51Z","timestamp":1775795811000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning\u2010based auto\u2010contouring for organs at risk in three\u2010dimensional image\u2010guided brachytherapy for cervical cancer and endometrial cancer"],"prefix":"10.1002","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2873-1171","authenticated-orcid":false,"given":"Kirika","family":"Takahashi","sequence":"first","affiliation":[{"name":"Department of Therapeutic Radiology Health Sciences Tohoku University Graduate School of Medicine  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ken","family":"Takeda","sequence":"additional","affiliation":[{"name":"Department of Therapeutic Radiology Health Sciences Tohoku University Graduate School of Medicine  Sendai Japan"},{"name":"Department of Radiation Oncology Tohoku University Graduate School of Medicine  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2389-2577","authenticated-orcid":false,"given":"Hisamichi","family":"Takagi","sequence":"additional","affiliation":[{"name":"Department of Therapeutic Radiology Health Sciences Tohoku University Graduate School of Medicine  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