{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:29:32Z","timestamp":1789122572086,"version":"build-2803163510"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014513","name":"Universitetet i Stavanger","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014513","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejropen.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology Open"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.ejro.2026.100738","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T07:55:08Z","timestamp":1772006108000},"page":"100738","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["AI-based prostate volume estimation from multi-planar MRI under variable acquisition protocols"],"prefix":"10.1016","volume":"16","author":[{"given":"Yao","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim Nikolaas","family":"Lindeijer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tord Martin","family":"Ytredal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas Bremset","family":"Alvestad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7544-0797","authenticated-orcid":false,"given":"Alvaro","family":"Fernandez-Quilez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejro.2026.100738_bib1","first-page":"7","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA A Cancer J. Clin."},{"key":"10.1016\/j.ejro.2026.100738_bib2","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.230138","article-title":"Deep learning prostate MRI segmentation accuracy and robustness: a systematic review","volume":"6","author":"Fassia","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"10.1016\/j.ejro.2026.100738_bib3","doi-asserted-by":"crossref","unstructured":"Fernandez-Quilez A., Nordstr\u00f6m T., J\u00e4derling F., et al Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection. Journal of Magnetic Resonance Imaging n\/a: https:\/\/doi.org\/10.1002\/jmri.29090.","DOI":"10.1002\/jmri.29090"},{"key":"10.1016\/j.ejro.2026.100738_bib4","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1186\/s13244-024-01863-w","article-title":"Impact of uncertainty quantification through conformal prediction on volume assessment from deep learning-based MRI prostate segmentation","volume":"15","author":"Gade","year":"2024","journal-title":"Insights Imaging"},{"key":"10.1016\/j.ejro.2026.100738_bib5","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1007\/s00330-022-09239-8","article-title":"Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI","volume":"33","author":"Thimansson","year":"2022","journal-title":"Eur. Radio."},{"key":"10.1016\/j.ejro.2026.100738_bib6","doi-asserted-by":"crossref","first-page":"4801","DOI":"10.1007\/s00330-023-10515-4","article-title":"Manual prostate MRI segmentation by readers with different experience: a study of the learning progress","volume":"34","author":"Langkilde","year":"2024","journal-title":"Eur. Radio."},{"key":"10.1016\/j.ejro.2026.100738_bib7","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.223128","article-title":"PI-RADS: where next?","volume":"307","author":"Turkbey","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.ejro.2026.100738_bib8","series-title":"Proceeding of 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"Contrasting axial T2W MRI for prostate cancer triage: a self-supervised learning approach","author":"Fernandez-Quilez","year":"2022"},{"key":"10.1016\/j.ejro.2026.100738_bib9","series-title":"Cancer Prevention, Detection, and Intervention","first-page":"73","article-title":"On Undesired Emergent Behaviors in Compound Prostate Cancer Detection Systems","author":"Sortland Rolfsnes","year":"2025"},{"key":"10.1016\/j.ejro.2026.100738_bib10","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.231130","article-title":"Global variation in magnetic resonance imaging quality of the prostate","volume":"309","author":"Giganti","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.ejro.2026.100738_bib11","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1148\/radiol.2017170129","article-title":"Abbreviated biparametric prostate MR imaging in men with elevated prostate-specific antigen","volume":"285","author":"Kuhl","year":"2017","journal-title":"Radiology"},{"key":"10.1016\/j.ejro.2026.100738_bib12","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.230138","article-title":"Deep learning prostate MRI segmentation accuracy and robustness: a systematic review","volume":"6","author":"Fassia","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"10.1016\/j.ejro.2026.100738_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105821","article-title":"Anisotropic 3D multi-stream CNN for accurate prostate segmentation from multi-planar MRI","volume":"200","author":"Meyer","year":"2021","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/j.ejro.2026.100738_bib14","series-title":"Proceeding of 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","first-page":"1915","article-title":"Improving Prostate Whole Gland Segmentation In T2-Weighted MRI With Synthetically Generated Data","author":"Fernandez-Quilez","year":"2021"},{"key":"10.1016\/j.ejro.2026.100738_bib15","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.ejro.2026.100738_bib16","doi-asserted-by":"crossref","first-page":"5087","DOI":"10.1007\/s00330-023-09410-9","article-title":"Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks","volume":"33","author":"Jimenez-Pastor","year":"2023","journal-title":"Eur. Radio."},{"key":"10.1016\/j.ejro.2026.100738_bib17","series-title":"Proceeding of 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"177","article-title":"Automatic high resolution segmentation of the prostate from multi-planar MRI","author":"Meyer","year":"2018"},{"key":"10.1016\/j.ejro.2026.100738_bib18","article-title":"Prostate cancer risk stratification and scan tailoring using deep learning on abbreviated prostate MRI","volume":"29798","author":"Johnson","year":"2025","journal-title":"Magn. Reson. Imaging Jmri"},{"key":"10.1016\/j.ejro.2026.100738_bib19","article-title":"Informed machine learning -- a taxonomy and survey of integrating knowledge into learning systems","author":"Rueden","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.ejro.2026.100738_bib20","series-title":"International Joint Conference on Neural Networks (IJCNN)","first-page":"1","article-title":"How Does Knowledge Injection Help in Informed Machine Learning? In: 2023","author":"Von Rueden","year":"2023"},{"key":"10.1016\/j.ejro.2026.100738_bib21","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/S1470-2045(24)00220-1","article-title":"Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study","volume":"25","author":"Saha","year":"2024","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.ejro.2026.100738_bib22","author":"Lindeijer","year":"2023","journal-title":"Leverag.. multiview data Annot. Prostate MRI Segm. A Contrastive Approach"},{"key":"10.1016\/j.ejro.2026.100738_bib23","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.eururo.2019.02.033","article-title":"Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2","volume":"76","author":"Turkbey","year":"2019","journal-title":"Eur. Urol."},{"key":"10.1016\/j.ejro.2026.100738_bib24","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1002\/jmri.27585","article-title":"Deep learning whole-gland and zonal prostate segmentation on a public mri dataset","volume":"54","author":"Cuocolo","year":"2021","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.ejro.2026.100738_bib25","series-title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","first-page":"200","article-title":"Centreline-based shape descriptors of the left atrial appendage in relation with thrombus formation","author":"Genua","year":"2019"},{"key":"10.1016\/j.ejro.2026.100738_bib26","series-title":"Proceeding of 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"Multi-planar T2W MRI for an improved prostate cancer lesion classification","author":"Fernandez-Quilez","year":"2022"},{"key":"10.1016\/j.ejro.2026.100738_bib27","author":"Fang","year":"2021","journal-title":"Increm. Crossview Mutual Distill. Selfsupervised Med. CT Synth."},{"key":"10.1016\/j.ejro.2026.100738_bib28","doi-asserted-by":"crossref","first-page":"27066","DOI":"10.1109\/ACCESS.2023.3257767","article-title":"CrossTransUnet: a new computationally inexpensive tumor segmentation model for brain MRI","volume":"11","author":"Anaya-Isaza","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.ejro.2026.100738_bib29","series-title":"Machine Learning in Medical Imaging","first-page":"217","article-title":"Prostate Segmentation Using Multiparametric and Multiplanar Magnetic Resonance Images","author":"Shanmugalingam","year":"2024"},{"key":"10.1016\/j.ejro.2026.100738_bib30","article-title":"Decision Strategies in AI-Based Ensemble Models in Opportunistic Alzheimer\u2019s Detection from Structural MRI","author":"Hammonds","year":"2025","journal-title":"J. Digit Imaging Inf. Med."},{"key":"10.1016\/j.ejro.2026.100738_bib31","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1038\/s41746-024-01328-w","article-title":"Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation","volume":"7","author":"Chen","year":"2024","journal-title":"npj Digit Med."},{"key":"10.1016\/j.ejro.2026.100738_bib32","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1007\/s00330-023-09967-5","article-title":"Trust and stakeholder perspectives on the implementation of AI tools in clinical radiology","volume":"34","author":"Bergquist","year":"2023","journal-title":"Eur. Radio."},{"key":"10.1016\/j.ejro.2026.100738_bib33","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s43681-022-00161-9","article-title":"Deep learning in radiology: ethics of data and on the value of algorithm transparency, interpretability and explainability","volume":"3","author":"Fernandez-Quilez","year":"2023","journal-title":"AI Ethics"},{"key":"10.1016\/j.ejro.2026.100738_bib34","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.250325","article-title":"Sustainability in radiology: position paper and call to action from ACR, AOSR, ASR, CAR, CIR, ESR, ESRNM, ISR, IS3R, RANZCR, and RSNA","volume":"314","author":"Rockall","year":"2025","journal-title":"Radiology"}],"container-title":["European Journal of Radiology Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352047726000158?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352047726000158?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T20:31:00Z","timestamp":1784320260000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352047726000158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":34,"alternative-id":["S2352047726000158"],"URL":"https:\/\/doi.org\/10.1016\/j.ejro.2026.100738","relation":{},"ISSN":["2352-0477"],"issn-type":[{"value":"2352-0477","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AI-based prostate volume estimation from multi-planar MRI under variable acquisition protocols","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology Open","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejro.2026.100738","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"100738"}}