{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T20:45:06Z","timestamp":1793825106515,"version":"4.6.1"},"reference-count":29,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","meddos.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Dosimetry"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1016\/j.meddos.2024.09.002","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T17:18:54Z","timestamp":1728407934000},"page":"91-95","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"title":["Evaluation of the accuracy of automated segmentation based on deep learning for prostate cancer patients"],"prefix":"10.1016","volume":"50","author":[{"given":"Hideharu","family":"Miura","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soichiro","family":"Ishihara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Kenjo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5473-4956","authenticated-orcid":false,"given":"Minoru","family":"Nakao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5459-9846","authenticated-orcid":false,"given":"Shuichi","family":"Ozawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masayuki","family":"Kagemoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.meddos.2024.09.002_bib0001","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3322\/caac.21763","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J Clin"},{"key":"10.1016\/j.meddos.2024.09.002_bib0002","article-title":"Prostate cancer incidence and mortality: Global status and temporal trends in 89 countries from 2000 to 2019","volume":"10","author":"Wang","year":"2022","journal-title":"Front Public Health"},{"issue":"1","key":"10.1016\/j.meddos.2024.09.002_bib0003","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s13014-021-01831-4","article-title":"Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: A workflow study at two cancer centers","volume":"16","author":"Wong","year":"2021","journal-title":"Radiat Oncol"},{"issue":"1","key":"10.1016\/j.meddos.2024.09.002_bib0004","doi-asserted-by":"crossref","first-page":"e80","DOI":"10.1016\/j.prro.2020.05.013","article-title":"Clinical evaluation of deep learning and atlas-based auto-contouring of bladder and rectum for prostate radiation therapy","volume":"11","author":"Zabel","year":"2021","journal-title":"Pract Radiat Oncol"},{"issue":"1","key":"10.1016\/j.meddos.2024.09.002_bib0005","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1186\/s13014-021-01896-1","article-title":"Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models","volume":"16","author":"Urago","year":"2021","journal-title":"Radiat Oncol"},{"issue":"4","key":"10.1016\/j.meddos.2024.09.002_bib0006","doi-asserted-by":"crossref","first-page":"2570","DOI":"10.1002\/mp.15525","article-title":"Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process","volume":"49","author":"Duan","year":"2022","journal-title":"Med Phys"},{"key":"10.1016\/j.meddos.2024.09.002_bib0007","article-title":"Real-world validation of artificial intelligence-based computed tomography auto-contouring for prostate cancer radiotherapy planning","volume":"28","author":"Palazzo","year":"2023","journal-title":"Phys Imaging Radiat Oncol"},{"issue":"11","key":"10.1016\/j.meddos.2024.09.002_bib0008","doi-asserted-by":"crossref","first-page":"e14090","DOI":"10.1002\/acm2.14090","article-title":"Feasibility evaluation of novel AI-based deep-learning contouring algorithm for radiotherapy","volume":"24","author":"Maduro Bustos","year":"2023","journal-title":"J Appl Clin Med Phys"},{"issue":"3","key":"10.1016\/j.meddos.2024.09.002_bib0009","article-title":"Clinical validation of Siemens' Syngo.via automatic contouring system","volume":"8","author":"Pera","year":"2023","journal-title":"Adv Radiat Oncol"},{"issue":"7","key":"10.1016\/j.meddos.2024.09.002_bib0010","doi-asserted-by":"crossref","first-page":"4079","DOI":"10.1002\/mp.16537","article-title":"Incremental retraining, clinical implementation, and acceptance rate of deep learning auto-segmentation for male pelvis in a multiuser environment","volume":"50","author":"Duan","year":"2023","journal-title":"Med Phys"},{"issue":"11","key":"10.1016\/j.meddos.2024.09.002_bib0011","doi-asserted-by":"crossref","first-page":"959","DOI":"10.3390\/diagnostics10110959","article-title":"A deep learning-based automated CT segmentation of prostate cancer anatomy for radiation therapy planning-A Retrospective Multicenter Study","volume":"10","author":"Kiljunen","year":"2020","journal-title":"Diagnostics (Basel)"},{"key":"10.1016\/j.meddos.2024.09.002_bib0012","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.phro.2022.04.007","article-title":"Autosegmentation based on different-sized training datasets of consistently-curated volumes and impact on rectal contours in prostate cancer radiation therapy","volume":"22","author":"Elisabeth Olsson","year":"2022","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.meddos.2024.09.002_bib0013","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2023.1089807","article-title":"How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images","volume":"13","author":"Strolin","year":"2023","journal-title":"Front Oncol"},{"key":"10.1016\/j.meddos.2024.09.002_bib0014","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2023.1213068","article-title":"A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy","volume":"13","author":"Doolan","year":"2023","journal-title":"Front Oncol"},{"key":"10.1016\/j.meddos.2024.09.002_bib0015","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.radonc.2019.10.019","article-title":"Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning","volume":"144","author":"Wong","year":"2020","journal-title":"Radiother Oncol"},{"issue":"24","key":"10.1016\/j.meddos.2024.09.002_bib0016","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/aaf11c","article-title":"Fully automated organ segmentation in male pelvic CT images","volume":"63","author":"Balagopal","year":"2018","journal-title":"Phys Med Biol"},{"issue":"4","key":"10.1016\/j.meddos.2024.09.002_bib0017","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1016\/j.ijrobp.2019.03.017","article-title":"Automatic segmentation of the prostate on CT images using deep neural networks (DNN)","volume":"104","author":"Liu","year":"2019","journal-title":"Int J Radiat Oncol Biol Phys"},{"issue":"1","key":"10.1016\/j.meddos.2024.09.002_bib0018","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s13014-022-01985-9","article-title":"Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer","volume":"17","author":"Kawula","year":"2022","journal-title":"Radiat Oncol"},{"issue":"3","key":"10.1016\/j.meddos.2024.09.002_bib0019","doi-asserted-by":"crossref","first-page":"e353","DOI":"10.1016\/j.ijrobp.2012.01.023","article-title":"Pelvic normal tissue contouring guidelines for radiation therapy: A Radiation Therapy Oncology Group consensus panel atlas","volume":"83","author":"Gay","year":"2012","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.meddos.2024.09.002_bib0020","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.radonc.2020.05.038","article-title":"Organ at risk delineation for radiation therapy clinical trials: Global Harmonization Group consensus guidelines","volume":"150","author":"Mir","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.meddos.2024.09.002_bib0021","unstructured":"https:\/\/ourworldindata.org\/obesity (Accessed January 3, 2023.)"},{"issue":"1","key":"10.1016\/j.meddos.2024.09.002_bib0022","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.meddos.2021.08.001","article-title":"Treatment planning comparison between dynamic wave arc and volumetric modulated arc therapies for prostate-cancer treatment","volume":"47","author":"Miura","year":"2022","journal-title":"Med Dosim"},{"key":"10.1016\/j.meddos.2024.09.002_bib0023","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.radonc.2021.05.003","article-title":"Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review","volume":"160","author":"Sherer","year":"2021","journal-title":"Radiother Oncol"},{"issue":"6","key":"10.1016\/j.meddos.2024.09.002_bib0024","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.clon.2023.01.016","article-title":"A review of the metrics used to assess auto-contouring systems in radiotherapy","volume":"35","author":"Mackay","year":"2023","journal-title":"Clin Oncol (R Coll Radiol)"},{"key":"10.1016\/j.meddos.2024.09.002_bib0025","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.radonc.2019.09.022","article-title":"Improving automatic delineation for head and neck organs at risk by deep learning contouring","volume":"142","author":"van Dijk","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.meddos.2024.09.002_bib0026","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1186\/1748-717X-8-188","article-title":"Creation of RTOG compliant patient CT-atlases for automated atlas based contouring of local regional breast and high-risk prostate cancers","volume":"8","author":"Velker","year":"2013","journal-title":"Radiat Oncol"},{"key":"10.1016\/j.meddos.2024.09.002_bib0027","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1186\/1748-717X-6-123","article-title":"Inter-observer variability in contouring the penile bulb on CT images for prostate cancer treatment planning","volume":"6","author":"Perna","year":"2011","journal-title":"Radiat Oncol"},{"issue":"3","key":"10.1016\/j.meddos.2024.09.002_bib0028","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0167-8140(98)00021-8","article-title":"Intra- and inter-observer variability in contouring prostate and seminal vesicles: Implications for conformal treatment planning","volume":"47","author":"Fiorino","year":"1998","journal-title":"Radiother Oncol"},{"issue":"5","key":"10.1016\/j.meddos.2024.09.002_bib0029","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1016\/j.ijrobp.2011.02.050","article-title":"Prostate contouring variation: Can it be fixed?","volume":"82","author":"Khoo","year":"2012","journal-title":"Int J Radiat Oncol Biol Phys"}],"container-title":["Medical Dosimetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0958394724000487?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0958394724000487?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:15:20Z","timestamp":1738268120000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0958394724000487"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025]]}},"alternative-id":["S0958394724000487"],"URL":"https:\/\/doi.org\/10.1016\/j.meddos.2024.09.002","relation":{},"ISSN":["0958-3947"],"issn-type":[{"value":"0958-3947","type":"print"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evaluation of the accuracy of automated segmentation based on deep learning for prostate cancer patients","name":"articletitle","label":"Article Title"},{"value":"Medical Dosimetry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.meddos.2024.09.002","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}