{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T22:02:07Z","timestamp":1790287327936,"version":"4.1.0"},"reference-count":53,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T00:00:00Z","timestamp":1638748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/R512400\/1"],"award-info":[{"award-number":["EP\/R512400\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCa-Segmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and Report-Generator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experiment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections.<\/jats:p>","DOI":"10.3390\/cancers13236138","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T22:18:42Z","timestamp":1638829122000},"page":"6138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["AutoProstate: Towards Automated Reporting of Prostate MRI for Prostate Cancer Assessment Using Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Pritesh","family":"Mehta","sequence":"first","affiliation":[{"name":"Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK"},{"name":"School of Biomedical Engineering & Imaging Sciences, King\u2019s College London, London SE1 7EH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michela","family":"Antonelli","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering & Imaging Sciences, King\u2019s College London, London SE1 7EH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saurabh","family":"Singh","sequence":"additional","affiliation":[{"name":"Centre for Medical Imaging, University College London, London WC1E 6BT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7907-1976","authenticated-orcid":false,"given":"Natalia","family":"Grondecka","sequence":"additional","affiliation":[{"name":"Department of Medical Radiology, Medical University of Lublin, 20-059 Lublin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8504-2968","authenticated-orcid":false,"given":"Edward W.","family":"Johnston","sequence":"additional","affiliation":[{"name":"Interventional Radiology, Royal Marsden Hospital, London SW3 6JJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hashim U.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Imperial Prostate, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Emberton","sequence":"additional","affiliation":[{"name":"Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, London WC1E 6BT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shonit","family":"Punwani","sequence":"additional","affiliation":[{"name":"Centre for Medical Imaging, University College London, London WC1E 6BT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00e9bastien","family":"Ourselin","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering & Imaging Sciences, King\u2019s College London, London SE1 7EH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1016\/S0140-6736(16)32401-1","article-title":"Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): A paired validating confirmatory study","volume":"389","author":"Ahmed","year":"2017","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1007\/s00330-019-06654-2","article-title":"Interreader variability in prostate MRI reporting using Prostate Imaging Reporting and Data System version 2.1","volume":"30","author":"Brembilla","year":"2020","journal-title":"Eur. 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