{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T12:18:31Z","timestamp":1783945111423,"version":"3.55.0"},"reference-count":52,"publisher":"MJS Publishing, Medical Journals Sweden AB","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SJU"],"abstract":"<jats:p>Background and aim: Magnetic resonance imaging (MRI) is crucial for prostate cancer (Pca) diagnosis, risk stratification, and treatment planning. However, large-scale observational studies require structured MRI data, which are often only obtainable from free-text reports. We aimed to extract information from narrative prostate MRI reports and to describe subsequent biopsy outcomes in a nationwide population-based cohort.\nMethods: We identified 108,361 prostate MRI examinations in Prostate Cancer database Sweden with extended treatments and endpoints data (PCBase Xtend) performed in 2015\u20132023. A rule-based text recognition algorithm was created and used to extract Prostate Imaging Reporting and Data System (PI-RADS) score and prostate volume from free-text MRI reports. Extracted data were validated against manually extracted information in the National Prostate Cancer Register (NPCR). We examined biopsy rates and Gleason score according to PI-RADS, Prostate Specific Antigen (PSA) density, and calendar year.\nResults: The proportion of reports with identifiable PI-RADS scores increased from 38% in 2015\u20132016 to 83% in 2022\u20132023, with excellent agreement with NPCR data (correlation coefficient r = 0.94). Extracted prostate volumes correlated well with those in NPCR (r = 0.88). Biopsy rates decreased for PI-RADS 3 lesions over time, particularly in men with PSA density &lt;\u20090.15 ng\/ml\/ml, while the proportion of men with PI-RADS 5 lesions who underwent biopsy increased. Almost all prostate cancers in men with PI-RADS 3 lesions were Gleason 6 or 7 (3+4). Gleason 9\u201310 was almost exclusively found in PI-RADS 5 lesions.\nConclusions: Automated extraction of information from unstructured MRI reports is feasible and accurate. The observed temporal trends reflecting increasing quality and standardization of prostate MRI support its use in large-scale epidemiological research.<\/jats:p>","DOI":"10.2340\/sju.v61.45540","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:41:22Z","timestamp":1774449682000},"page":"64-71","source":"Crossref","is-referenced-by-count":2,"title":["Nationwide population-based longitudinal data on magnetic resonance imaging of the prostate and subsequent prostate biopsy results"],"prefix":"10.2340","volume":"61","author":[{"given":"Eugenio","family":"Ventimiglia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rolf","family":"Gedeborg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus","family":"Westerberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Zaurito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fredrik","family":"J\u00e4derling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P\u00e4r","family":"Stattin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans","family":"Garmo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1183","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"146499","doi-asserted-by":"crossref","unstructured":"Stabile A, Mazzone E, Cirulli GO, et al: Association Between Multiparametric Magnetic Resonance Imaging of the Prostate and Oncological Outcomes after Primary Treatment for Prostate Cancer: A Systematic Review and Meta-analysis. 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Med. 2018; 378: 1767\u20131777."},{"key":"146504","doi-asserted-by":"crossref","unstructured":"https:\/\/doi.org\/10.1056\/NEJMoa1801993","DOI":"10.1056\/NEJMoa1801993"},{"key":"146505","doi-asserted-by":"crossref","unstructured":"Giganti F, Rosenkrantz AB, Villeirs G, et al: The Evolution of MRI of the Prostate: The Past, the Present, and the Future. AJR Am. J. Roentgenol. 2019; 213: 384\u2013396.","DOI":"10.2214\/AJR.18.20796"},{"key":"146506","doi-asserted-by":"crossref","unstructured":"https:\/\/doi.org\/10.2214\/AJR.18.20796","DOI":"10.2214\/AJR.18.20796"},{"key":"146507","doi-asserted-by":"crossref","unstructured":"Stabile A, Giganti F, Kasivisvanathan V, et al: Factors Influencing Variability in the Performance of Multiparametric Magnetic Resonance Imaging in Detecting Clinically Significant Prostate Cancer: A Systematic Literature Review. Eur. Urol. 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