{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T08:51:21Z","timestamp":1773391881854,"version":"3.50.1"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"3\u20134","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005689","name":"G\u00f6teborgs L\u00e4kars\u00e4llskap","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005689","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University Hospital of Sahlgrenska"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study aimed to evaluate the clinical acceptability of artificial intelligence (AI)-based organ segmentations on pretreatment CT images of prostate cancer patients using manual organ delineations as a reference. Paired AI-based segmentations and manual delineations of the prostate, urinary bladder, and rectum were evaluated by three observers, according to a 4-grade Likert-scale, based on quality criteria, developed through a Delphi process. Visual grading characteristics (VGC) analysis was performed. When comparing the ratings of AI-based (n\u00a0=\u00a0360) and manual delineations (n\u00a0=\u00a0360), the area under the VGC-curve (AUCVGC) was 0.36 (95% CI 0.27\u20130.44), 0.35 (95% CI 0.28\u20130.41), and 0.3 (95% CI 0.22\u20130.40) for the prostate, urinary bladder, and rectum, respectively, indicating inferior ratings for the algorithm. Few AI segmentations (8%) were considered clinically unacceptable, while in 67% no or minor changes were needed. Despite superior ratings for manual delineations, most AI-segmentations needed no or minor changes, indicating clinical acceptability.<\/jats:p>","DOI":"10.1093\/rpd\/ncaf184","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T12:58:26Z","timestamp":1766149106000},"page":"204-213","source":"Crossref","is-referenced-by-count":0,"title":["Comparison between artificial intelligence-based and manual organ delineations in pretreatment computed tomography scans of prostate cancer patients: a visual grading study"],"prefix":"10.1093","volume":"202","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3138-9330","authenticated-orcid":false,"given":"Eirini","family":"Polymeri","sequence":"first","affiliation":[{"name":"Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Medicinaregatan 3, Medicinareberget, 413 90, Gothenburg, Region V\u00e4stra G\u00f6taland ,","place":["Sweden"]},{"name":"Department of Radiology, Sahlgrenska University Hospital , Bl\u00e5 Str\u00e5ket 5, Region V\u00e4stra G\u00f6taland, 413 45, Gothenburg ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c5se A","family":"Johnsson","sequence":"additional","affiliation":[{"name":"Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Medicinaregatan 3, Medicinareberget, 413 90, Gothenburg, Region V\u00e4stra G\u00f6taland ,","place":["Sweden"]},{"name":"Department of Radiology, Sahlgrenska University Hospital , Bl\u00e5 Str\u00e5ket 5, Region V\u00e4stra G\u00f6taland, 413 45, Gothenburg ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olof","family":"Enqvist","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Chalmers University of Technology , H\u00f6rsalsv\u00e4gen 9-11, Region V\u00e4stra G\u00f6taland, 412 96, Gothenburg ,","place":["Sweden"]},{"name":"Eigenvision AB , Bredgatan 4, 211 30, Region Sk\u00e5ne, Malm\u00f6 ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Ul\u00e9n","sequence":"additional","affiliation":[{"name":"Eigenvision AB , Bredgatan 4, 211 30, Region Sk\u00e5ne, Malm\u00f6 ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jon","family":"Kindblom","sequence":"additional","affiliation":[{"name":"Department of Oncology, Sahlgrenska University Hospital , Bl\u00e5 Str\u00e5ket 2, Region V\u00e4stra G\u00f6taland, 413 45, Gothenburg ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karin","family":"Braide","sequence":"additional","affiliation":[{"name":"Department of Oncology, Institute of Clinical Sciences, University of Gothenburg , Medicinaregatan 3, Medicinareberget, 413 90, Gothenburg, Region V\u00e4stra G\u00f6taland ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hans-Jurgen","family":"Wiltz","sequence":"additional","affiliation":[{"name":"Department of Oncology and Radiotherapy, Region Kronoberg, Central lasarett V\u00e4xj\u00f6 , Strandv\u00e4gen 8, 351 85, V\u00e4xj\u00f6 ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margareta","family":"Tanyasiov\u00e1","sequence":"additional","affiliation":[{"name":"Department of Oncology, Sahlgrenska University Hospital , Bl\u00e5 Str\u00e5ket 2, Region V\u00e4stra G\u00f6taland, 413 45, Gothenburg ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elin","family":"Tr\u00e4g\u00e5rdh","sequence":"additional","affiliation":[{"name":"Clinical Physiology and Nuclear Medicine, Lund University and Sk\u00e5ne University Hospital , 221 85, Malm\u00f6, Region Sk\u00e5ne ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lars","family":"Edenbrandt","sequence":"additional","affiliation":[{"name":"Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg , Bl\u00e5 Str\u00e5ket 5, 413 45, Region V\u00e4stra G\u00f6taland, Gothenburg ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henrik","family":"Kj\u00f6lhede","sequence":"additional","affiliation":[{"name":"Department of Urology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Medicinaregatan 5, Medicinareberget, 413 90, Region V\u00e4stra G\u00f6taland, Gothenburg ,","place":["Sweden"]},{"name":"Department of Urology, Sahlgrenska University Hospital , Bl\u00e5 Str\u00e5ket 5, 413 45, Region V\u00e4stra G\u00f6taland, Gothenburg ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angelica","family":"Svalkvist","sequence":"additional","affiliation":[{"name":"Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg , Medicinaregatan 5, Medicinareberget, 413 90, Region V\u00e4stra G\u00f6taland, Gothenburg ,","place":["Sweden"]},{"name":"Department of Biomedical Engineering and Medical Physics, Sahlgrenska University Hospital , Bl\u00e5 Str\u00e5ket 5, 413 45, Region V\u00e4stra G\u00f6taland, Gothenburg ,","place":["Sweden"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"2026031302442470800_ref1","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.jacr.2003.11.001","article-title":"Quality and variability in diagnostic radiology","volume":"1","author":"Alpert","year":"2004","journal-title":"J Am Coll Radiol"},{"key":"2026031302442470800_ref2","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1016\/j.diii.2020.09.001","article-title":"Interobserver agreement issues in radiology","volume":"101","author":"Benchoufi","year":"2020","journal-title":"Diagn Interv Imaging"},{"key":"2026031302442470800_ref3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.radonc.2012.08.008","article-title":"QA makes a clinical trial stronger: evidence-based medicine in radiation therapy","volume":"105","author":"Weber","year":"2012","journal-title":"Radiother Oncol"},{"key":"2026031302442470800_ref4","doi-asserted-by":"publisher","first-page":"2996","DOI":"10.1200\/JCO.2009.27.4498","article-title":"Critical impact of radiotherapy protocol compliance and quality in the treatment of advanced head and neck cancer: results from TROG 02.02","volume":"28","author":"Peters","year":"2010","journal-title":"J Clin Oncol"},{"key":"2026031302442470800_ref5","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1093\/jnci\/djt001","article-title":"Radiotherapy protocol deviations and clinical outcomes: a meta-analysis of cooperative group clinical trials","volume":"105","author":"Ohri","year":"2013","journal-title":"J Natl Cancer Inst"},{"key":"2026031302442470800_ref6","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1016\/j.ijrobp.2022.11.021","article-title":"Assessing Interobserver variability in the delineation of structures in radiation oncology: a systematic review","volume":"115","author":"Guzene","year":"2023","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"2026031302442470800_ref7","doi-asserted-by":"publisher","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"},{"key":"2026031302442470800_ref8","doi-asserted-by":"publisher","first-page":"136","DOI":"10.4103\/0971-6203.44472","article-title":"Tumor delineation: the weakest link in the search for accuracy in radiotherapy","volume":"33","author":"Njeh","year":"2008","journal-title":"J Med Phys"},{"key":"2026031302442470800_ref9","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1111\/1754-9485.12462","article-title":"A review of interventions to reduce inter-observer variability in volume delineation in radiation oncology","volume":"60","author":"Vinod","year":"2016","journal-title":"J Med Imaging Radiat Oncol"},{"key":"2026031302442470800_ref10","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.radonc.2020.09.008","article-title":"Overview of artificial intelligence-based applications in radiotherapy: recommendations for implementation and quality assurance","volume":"153","author":"Vandewinckele","year":"2020","journal-title":"Radiother Oncol"},{"key":"2026031302442470800_ref11","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/s13014-021-01771-z","article-title":"Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery","volume":"16","author":"Chung","year":"2021","journal-title":"Radiat Oncol"},{"key":"2026031302442470800_ref12","doi-asserted-by":"publisher","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":"2026031302442470800_ref13","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1002\/acm2.13446","article-title":"Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy","volume":"22","author":"Jamtheim Gustafsson","year":"2021","journal-title":"J Appl Clin Med Phys"},{"key":"2026031302442470800_ref14","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1111\/1754-9485.13286","article-title":"Deep learning for segmentation in radiation therapy planning: a review","volume":"65","author":"Samarasinghe","year":"2021","journal-title":"J Med Imaging Radiat Oncol"},{"key":"2026031302442470800_ref15","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15174389","article-title":"Automatic segmentation with deep learning in radiotherapy","volume":"15","author":"Isaksson","year":"2023","journal-title":"Cancers (Basel)"},{"key":"2026031302442470800_ref16","doi-asserted-by":"publisher","first-page":"105279","DOI":"10.1016\/j.ijmedinf.2023.105279","article-title":"Research related to the diagnosis of prostate cancer based on machine learning medical images: a review","volume":"181","author":"Chen","year":"2024","journal-title":"Int J Med Inform"},{"key":"2026031302442470800_ref17","doi-asserted-by":"publisher","first-page":"100501","DOI":"10.1016\/j.phro.2023.100501","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"},{"key":"2026031302442470800_ref18","doi-asserted-by":"publisher","first-page":"055003","DOI":"10.1088\/2057-1976\/aad100","article-title":"Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning","volume":"4","author":"Kazemifar","year":"2018","journal-title":"Biomed Phys Eng Express"},{"key":"2026031302442470800_ref19","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.phro.2018.02.003","article-title":"Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy","volume":"5","author":"Astaraki","year":"2018","journal-title":"Phys Imaging Radiat Oncol"},{"key":"2026031302442470800_ref20","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.radonc.2021.04.019","article-title":"A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy","volume":"160","author":"Chen","year":"2021","journal-title":"Radiother Oncol"},{"key":"2026031302442470800_ref21","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.ctro.2020.09.004","article-title":"Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth","volume":"25","author":"Sartor","year":"2020","journal-title":"Clin Transl Radiat Oncol"},{"key":"2026031302442470800_ref22","doi-asserted-by":"publisher","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":"2026031302442470800_ref23","first-page":"100796","article-title":"Clinical evaluation of a deep learning CBCT auto-segmentation software for prostate adaptive radiation therapy","volume":"47","author":"Radici","year":"2024","journal-title":"Clin Transl Radiat Oncol"},{"key":"2026031302442470800_ref24","doi-asserted-by":"publisher","first-page":"101383","DOI":"10.1016\/j.adro.2023.101383","article-title":"Artificial intelligence-based organ delineation for radiation treatment planning of prostate cancer on computed tomography. Advances","volume":"9","author":"Polymeri","year":"2024","journal-title":"Radiat Oncol"},{"key":"2026031302442470800_ref25","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1259\/bjr\/35012658","article-title":"Visual grading characteristics (VGC) analysis: a non-parametric rank-invariant statistical method for image quality evaluation","volume":"80","author":"B\u00e5th","year":"2007","journal-title":"Br J Radiol"},{"key":"2026031302442470800_ref26","doi-asserted-by":"publisher","first-page":"e13871","DOI":"10.1002\/acm2.13871","article-title":"Evaluation of deep-learning image reconstruction for chest CT examinations at two different dose levels","volume":"24","author":"Svalkvist","year":"2023","journal-title":"J Appl Clin Med Phys"},{"key":"2026031302442470800_ref27","doi-asserted-by":"publisher","first-page":"e0225673","DOI":"10.1371\/journal.pone.0225673","article-title":"4D perfusion CT of prostate cancer for image-guided radiotherapy planning: a proof of concept study","volume":"14","author":"Beer","year":"2019","journal-title":"PLoS One"},{"key":"2026031302442470800_ref28","doi-asserted-by":"publisher","first-page":"1634","DOI":"10.1007\/s00330-017-5120-5","article-title":"3D T2-weighted imaging to shorten multiparametric prostate MRI protocols","volume":"28","author":"Polanec","year":"2018","journal-title":"Eur Radiol"},{"key":"2026031302442470800_ref29","doi-asserted-by":"publisher","first-page":"110145","DOI":"10.1016\/j.ejrad.2021.110145","article-title":"Rectal preparation significantly improves prostate imaging quality: assessment of the PI-QUAL score with visual grading characteristics","volume":"147","author":"Arnoldner","year":"2022","journal-title":"Eur J Radiol"},{"key":"2026031302442470800_ref30","doi-asserted-by":"publisher","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"},{"key":"2026031302442470800_ref31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.radonc.2021.02.040","article-title":"Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy","volume":"159","author":"Cha","year":"2021","journal-title":"Radiother Oncol"},{"key":"2026031302442470800_ref32","doi-asserted-by":"publisher","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":"2026031302442470800_ref33","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s40658-020-00316-9","article-title":"RECOMIA\u2014a cloud-based platform for artificial intelligence research in nuclear medicine and radiology","volume":"7","author":"Tr\u00e4g\u00e5rdh","year":"2020","journal-title":"EJNMMI Phys"},{"key":"2026031302442470800_ref34","doi-asserted-by":"publisher","first-page":"116","DOI":"10.5662\/wjm.v11.i4.116","article-title":"Delphi methodology in healthcare research: how to decide its appropriateness","volume":"11","author":"Nasa","year":"2021","journal-title":"World J Methodol"},{"key":"2026031302442470800_ref35","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1093\/rpd\/ncv542","article-title":"VGC ANALYZER: a software for statistical analysis of fully crossed multiple-reader multiple-case visual grading characteristics studies","volume":"169","author":"B\u00e5th","year":"2016","journal-title":"Radiat Prot Dosim"},{"key":"2026031302442470800_ref36","doi-asserted-by":"publisher","first-page":"7172","DOI":"10.1002\/cam4.3386","article-title":"Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists","volume":"9","author":"Chen","year":"2020","journal-title":"Cancer Med"},{"key":"2026031302442470800_ref37","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.semradonc.2014.02.003","article-title":"The value of magnetic resonance imaging for radiotherapy planning","volume":"24","author":"Dirix","year":"2014","journal-title":"Semin Radiat Oncol"},{"key":"2026031302442470800_ref38","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.radonc.2018.01.014","article-title":"ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer","volume":"127","author":"Salembier","year":"2018","journal-title":"Radiother Oncol"},{"key":"2026031302442470800_ref39","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s00066-002-0907-x","article-title":"Interobserver comparison of CT and MRI-based prostate apex definition. Clinical relevance for conformal radiotherapy treatment planning","volume":"178","author":"Wachter","year":"2002","journal-title":"Strahlenther Onkol"},{"key":"2026031302442470800_ref40","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.semradonc.2005.01.005","article-title":"Target definition in prostate, head, and neck","volume":"15","author":"Rasch","year":"2005","journal-title":"Semin Radiat Oncol"},{"key":"2026031302442470800_ref41","doi-asserted-by":"publisher","first-page":"4429","DOI":"10.1088\/0031-9155\/60\/11\/4429","article-title":"Contouring variability of human- and deformable-generated contours in radiotherapy for prostate cancer","volume":"60","author":"Gardner","year":"2015","journal-title":"Phys Med Biol"},{"key":"2026031302442470800_ref42","doi-asserted-by":"publisher","first-page":"101470","DOI":"10.1016\/j.adro.2024.101470","article-title":"Review of deep learning based autosegmentation for clinical target volume: current status and future directions","volume":"9","author":"Matoska","year":"2024","journal-title":"Adv Radiat Oncol"},{"key":"2026031302442470800_ref43","doi-asserted-by":"publisher","first-page":"e14482","DOI":"10.1002\/acm2.14482","article-title":"Deep learning based clinical target volumes contouring for prostate cancer: easy and efficient application","volume":"25","author":"Wen","year":"2024","journal-title":"J Appl Clin Med Phys"},{"key":"2026031302442470800_ref44","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1007\/s11547-023-01690-x","article-title":"Clinical evaluation of deep learning-based automatic clinical target volume segmentation: a single-institution multi-site tumor experience","volume":"128","author":"Hou","year":"2023","journal-title":"Radiol Med"},{"key":"2026031302442470800_ref45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.radonc.2019.11.021","article-title":"Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT","volume":"145","author":"Schreier","year":"2020","journal-title":"Radiother Oncol"},{"key":"2026031302442470800_ref46","doi-asserted-by":"publisher","first-page":"e778","DOI":"10.7759\/cureus.778","article-title":"Fan Beam CT: a comparison of image quality and dose delivered between two differing CT imaging modalities","volume":"8","author":"Lechuga","year":"2016","journal-title":"Cureus."},{"key":"2026031302442470800_ref47","doi-asserted-by":"publisher","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"},{"key":"2026031302442470800_ref48","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s10606-023-09465-8","article-title":"Suspicious minds: the problem of trust and conversational agents","volume":"32","author":"Ivarsson","year":"2023","journal-title":"Comput Support Coop Work"},{"key":"2026031302442470800_ref49","doi-asserted-by":"publisher","first-page":"R151","DOI":"10.1088\/0031-9155\/53\/12\/R01","article-title":"Anatomical imaging for radiotherapy","volume":"53","author":"Evans","year":"2008","journal-title":"Phys Med Biol"},{"key":"2026031302442470800_ref50","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1053\/j.semnuclmed.2021.11.013","article-title":"Recent advancements in CT and MR imaging of prostate cancer","volume":"52","author":"Daryanani","year":"2022","journal-title":"Semin Nucl Med"},{"key":"2026031302442470800_ref51","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1016\/j.prro.2012.04.005","article-title":"Impact of magnetic resonance imaging on computed tomography-based treatment planning and acute toxicity for prostate cancer patients treated with intensity modulated radiation therapy","volume":"3","author":"Ali","year":"2013","journal-title":"Pract Radiat Oncol"},{"key":"2026031302442470800_ref52","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1016\/S0360-3016(03)01446-9","article-title":"Reduction of dose delivered to the rectum and bulb of the penis using MRI delineation for radiotherapy of the prostate","volume":"57","author":"Steenbakkers","year":"2003","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"2026031302442470800_ref53","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1148\/radiol.2017152877","article-title":"What are we missing? False-negative cancers at multiparametric MR imaging of the prostate","volume":"286","author":"Borofsky","year":"2018","journal-title":"Radiology"},{"key":"2026031302442470800_ref54","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s13244-021-01010-9","article-title":"Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology","volume":"12","author":"Montagne","year":"2021","journal-title":"Insights Imaging"}],"container-title":["Radiation Protection Dosimetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/rpd\/article-pdf\/202\/3%E2%80%934\/204\/67333336\/ncaf184.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/rpd\/article-pdf\/202\/3%E2%80%934\/204\/67333336\/ncaf184.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:44:29Z","timestamp":1773384269000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/rpd\/article\/202\/3%E2%80%934\/204\/8518644"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":54,"journal-issue":{"issue":"3\u20134","published-online":{"date-parts":[[2026,3,13]]},"published-print":{"date-parts":[[2026,3,13]]}},"URL":"https:\/\/doi.org\/10.1093\/rpd\/ncaf184","relation":{},"ISSN":["0144-8420","1742-3406"],"issn-type":[{"value":"0144-8420","type":"print"},{"value":"1742-3406","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,3]]},"published":{"date-parts":[[2026,3]]}}}