{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T03:28:17Z","timestamp":1788492497896,"version":"build-2803163510"},"reference-count":37,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T00:00:00Z","timestamp":1757030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T00:00:00Z","timestamp":1757030400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"NIH","award":["R01CA237269"],"award-info":[{"award-number":["R01CA237269"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Health"],"published-print":{"date-parts":[[2025,12,1]]},"DOI":"10.1088\/3049-477x\/adf076","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T11:23:12Z","timestamp":1757071392000},"page":"015010","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Incorporating physicians\u2019 contouring style into auto-segmentation of clinical target volume for post-operative prostate cancer radiotherapy using a language encoder"],"prefix":"10.1088","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6712-5823","authenticated-orcid":true,"given":"Hengrui","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0872-327X","authenticated-orcid":false,"given":"Chien-Yi","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3083-6752","authenticated-orcid":true,"given":"Steve","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9590-0655","authenticated-orcid":true,"given":"Dan","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"key":"mlhealthadf076bib1","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1056\/NEJMoa1113162","type":"journal-article","article-title":"Radical prostatectomy versus observation for localized prostate cancer","volume":"367","author":"Wilt","year":"2012","journal-title":"New Engl. J. Med."},{"key":"mlhealthadf076bib2","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1001\/jama.294.4.433","type":"journal-article","article-title":"Risk of prostate cancer\u2013specific mortality following biochemical recurrence after radical prostatectomy","volume":"294","author":"Freedland","year":"2005","journal-title":"JAMA"},{"key":"mlhealthadf076bib3","doi-asserted-by":"publisher","DOI":"10.3389\/fsurg.2021.691473","type":"journal-article","article-title":"Radiation therapy after radical prostatectomy: what has changed over time?","volume":"8","author":"Zattoni","year":"2021","journal-title":"Front. Surg."},{"key":"mlhealthadf076bib4","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.1002\/cncr.30620","type":"journal-article","article-title":"Adjuvant radiation therapy, androgen deprivation, and docetaxel for high\u2010risk prostate cancer postprostatectomy","volume":"123","author":"Hurwitz","year":"2017","journal-title":"Cancer"},{"key":"mlhealthadf076bib5","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.781040","type":"journal-article","article-title":"Post-operative radiotherapy in prostate cancer: is it time for a belt and braces approach?","volume":"11","author":"Giraud","year":"2021","journal-title":"Front. Oncol."},{"key":"mlhealthadf076bib6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ctro.2023.100638","type":"journal-article","article-title":"ESTRO ACROP guideline on prostate bed delineation for postoperative radiotherapy in prostate cancer","volume":"41","author":"Dal Pra","year":"2023","journal-title":"Clin. Transl. Radiat. Oncol."},{"key":"mlhealthadf076bib7","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1016\/j.ijrobp.2020.11.010","type":"journal-article","article-title":"Prostate bed delineation guidelines for postoperative radiation therapy: on behalf of the francophone group of urological radiation therapy","volume":"109","author":"Robin","year":"2021","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"mlhealthadf076bib8","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102195","type":"journal-article","article-title":"PSA-Net: deep learning-based physician style-aware segmentation network for postoperative prostate cancer clinical target volumes","volume":"121","author":"Balagopal","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"mlhealthadf076bib9","doi-asserted-by":"publisher","first-page":"e99","DOI":"10.1016\/j.ijrobp.2021.07.492","type":"journal-article","article-title":"Style adaptation algorithm for auto-delineation of rectal cancer clinical target volume with deep active learning and attention mechanism","volume":"111","author":"Wang","year":"2021","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"mlhealthadf076bib10","article-title":"Attention is all you need","author":"Vaswani","year":"2017","type":"preprint"},{"key":"mlhealthadf076bib11","doi-asserted-by":"publisher","first-page":"10012","DOI":"10.48550\/arXiv.2103.14030","type":"conference-proceedings","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"mlhealthadf076bib12","article-title":"Perceiver IO: a general architecture for structured inputs & outputs","author":"Jaegle","year":"2021","type":"preprint"},{"key":"mlhealthadf076bib13","doi-asserted-by":"publisher","first-page":"4015","DOI":"10.1109\/ICCV51070.2023","type":"conference-proceedings","article-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"mlhealthadf076bib14","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ada8f3","type":"journal-article","article-title":"Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer","volume":"6","author":"Zhao","year":"2025","journal-title":"Mach. Learn. Sci. Technol."},{"key":"mlhealthadf076bib15","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/adb371","type":"journal-article","article-title":"Medical image segmentation assisted with clinical inputs via language encoder in a deep learning framework","volume":"6","author":"Zhao","year":"2024","journal-title":"Mach. Learn. Sci. Technol."},{"key":"mlhealthadf076bib16","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2022.acl-long.551","type":"preprint","article-title":"Linkbert: pretraining language models with document links","author":"Yasunaga","year":"2022"},{"key":"mlhealthadf076bib17","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/MCG.2023.3333475","type":"journal-article","article-title":"A workflow to visually assess interobserver variability in medical image segmentation","volume":"44","author":"Bayat","year":"2024","journal-title":"IEEE Comput. Graph. Appl."},{"key":"mlhealthadf076bib18","doi-asserted-by":"publisher","first-page":"85","DOI":"10.2991\/jaims.d.210623.001","type":"journal-article","article-title":"Dosimetric impact of physician style variations in contouring CTV for postoperative prostate cancer: a deep learning\u2014based simulation study","volume":"2","author":"Balagopal","year":"2021","journal-title":"J. Artif. Intell. Med. Sci."},{"key":"mlhealthadf076bib19","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102195","type":"journal-article","article-title":"Psa-net: deep learning\u2013based physician style\u2013aware segmentation network for postoperative prostate cancer clinical target volumes","volume":"121","author":"Balagopal","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"mlhealthadf076bib20","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","type":"preprint"},{"key":"mlhealthadf076bib21","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021","type":"preprint"},{"key":"mlhealthadf076bib22","doi-asserted-by":"publisher","first-page":"p 2","DOI":"10.18653\/v1\/N19-1423","type":"conference-proceedings","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"Kenton","year":"2019"},{"key":"mlhealthadf076bib23","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","type":"journal-article","article-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"mlhealthadf076bib24","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2025.110740","type":"journal-article","article-title":"Large language model-augmented learning for auto-delineation of treatment targets in head-and-neck cancer radiotherapy","volume":"205","author":"Rajendran","year":"2025","journal-title":"Radiother. Oncol."},{"key":"mlhealthadf076bib25","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-58452-8_13","type":"preprint","article-title":"End-to-end object detection with transformers","author":"Carion","year":"2020"},{"key":"mlhealthadf076bib26","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV48922.2021.01196","type":"preprint","article-title":"Vision transformers for dense prediction","author":"Ranftl","year":"2021"},{"key":"mlhealthadf076bib27","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2201.09873","type":"journal-article","article-title":"Transformers in medical imaging: a survey","volume":"88","author":"Shamshad","year":"2023","journal-title":"Med. Image Anal."},{"key":"mlhealthadf076bib28","doi-asserted-by":"publisher","first-page":"12113","DOI":"10.1109\/TPAMI.2023.3275156","type":"journal-article","article-title":"Multimodal learning with transformers: a survey","volume":"45","author":"Xu","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"mlhealthadf076bib29","doi-asserted-by":"publisher","first-page":"18155","DOI":"10.48550\/arXiv.2112.02244","type":"conference-proceedings","article-title":"Lavt: language-aware vision transformer for referring image segmentation","author":"Yang","year":"2022"},{"key":"mlhealthadf076bib30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.2308.05305","type":"journal-article","article-title":"From CNN to transformer: a review of medical image segmentation models","volume":"37","author":"Yao","year":"2024","journal-title":"J. Imaging Inform. Med."},{"key":"mlhealthadf076bib31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41551-023-01045-x","type":"journal-article","article-title":"A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics","volume":"7","author":"Zhou","year":"2023","journal-title":"Nat. Biomed. Eng."},{"key":"mlhealthadf076bib32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3458754","type":"journal-article","article-title":"Domain-specific language model pretraining for biomedical natural language processing","volume":"3","author":"Gu","year":"2021","journal-title":"ACM Trans. Comput. Healthcare (HEALTH)"},{"key":"mlhealthadf076bib33","article-title":"Google\u2019s neural machine translation system: bridging the gap between human and machine translation","author":"Wu","year":"2016","type":"preprint"},{"key":"mlhealthadf076bib34","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1007\/s10479-011-0841-3","type":"journal-article","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"mlhealthadf076bib35","article-title":"UMAP: uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018","type":"preprint"},{"key":"mlhealthadf076bib36","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1007\/978-3-031-08999-2_22","type":"conference-proceedings","article-title":"Swin unetr: swin transformers for semantic segmentation of brain tumors in mri images","author":"Hatamizadeh","year":"2021"},{"key":"mlhealthadf076bib37","doi-asserted-by":"publisher","first-page":"21152","DOI":"10.48550\/arXiv.2301.00785","type":"conference-proceedings","article-title":"Clip-driven universal model for organ segmentation and tumor detection","author":"Liu","year":"2023"}],"container-title":["Machine Learning: Health"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/3049-477X\/adf076","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/3049-477X\/adf076\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/3049-477X\/adf076\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/3049-477X\/adf076\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T11:23:15Z","timestamp":1757071395000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/3049-477X\/adf076"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,5]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,9,5]]},"published-print":{"date-parts":[[2025,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/3049-477x\/adf076","relation":{},"ISSN":["3049-477X"],"issn-type":[{"value":"3049-477X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,5]]},"assertion":[{"value":"Incorporating physicians\u2019 contouring style into auto-segmentation of clinical target volume for post-operative prostate cancer radiotherapy using a language encoder","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Health","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-04-16","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-07-16","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-09-05","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}