{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T04:51:45Z","timestamp":1790743905245,"version":"4.1.0"},"reference-count":28,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T00:00:00Z","timestamp":1722038400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter\u2010 and intra\u2010observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic malignancies is both time\u2010consuming and labor\u2010intensive.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The purpose of this study was to develop a deep learning model of pelvic LNRs delineation for patients with pelvic cancers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Planning computed tomography (CT) studies of 160 patients with pelvic primary malignancies (including rectal, prostate, and cervical cancer) were retrospectively collected and divided into training set (<jats:italic>n<\/jats:italic>\u00a0=\u00a0120) and testing set (<jats:italic>n<\/jats:italic>\u00a0=\u00a040). Six pelvic LNRs, including abdominal presacral, pelvic presacral, internal iliac nodes, external iliac nodes, obturator nodes, and inguinal nodes were delineated by two radiation oncologists as ground truth (Gt) contours. The cascaded multi\u2010heads U\u2010net (CMU\u2010net) was constructed based on the Gt contours from training cohort, which was subsequently verified in the testing cohort. The automatic delineation of six LNRs (Auto) was evaluated using dice similarity coefficient (DSC), average surface distance (ASD), 95th percentile Hausdorff distance (HD95), and a 7\u2010point scale score.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the testing set, the DSC of six pelvic LNRs by CMU\u2010net model varied from 0.851 to 0.942, ASD from 0.381 to 1.037\u00a0mm, and HD95 from 2.025 to 3.697\u00a0mm. No significant differences were founded in these three parameters between postoperative and preoperative cases. 95.9% and 96.2% of auto delineations by CMU\u2010net model got a score of 1\u20133 by two expert radiation oncologists, respectively, meaning only minor edits needed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The CMU\u2010net was successfully developed for automated delineation of pelvic LNRs for pelvic malignancies radiotherapy with improved contouring efficiency and highly consistent, which might justify its implementation in radiotherapy work flow.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17330","type":"journal-article","created":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T18:49:31Z","timestamp":1722106171000},"page":"7057-7066","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Efficient application of deep learning\u2010based elective lymph node regions delineation for pelvic malignancies"],"prefix":"10.1002","volume":"51","author":[{"given":"Feng","family":"Wen","sequence":"first","affiliation":[{"name":"Division of Abdominal Tumor Multimodality Treatment Department of Radiation Oncology Cancer Center West China Hospital, Sichuan University  Chengdu China"},{"name":"Med\u2010X Center for Informatics Sichuan University  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Lung Cancer Center West China Hospital, Sichuan University  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhebin","family":"Chen","sequence":"additional","affiliation":[{"name":"Chengdu Institute of Compute Application Chinese Academy of Sciences  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Dou","sequence":"additional","affiliation":[{"name":"Chengdu Institute of Compute Application Chinese 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