{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:03:30Z","timestamp":1792109010765,"version":"4.4.0"},"reference-count":45,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T00:00:00Z","timestamp":1720915200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJU International"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To assess artificial intelligence (AI) ability to evaluate intraprostatic prostate cancer (PCa) on prostate\u2010specific membrane antigen positron emission tomography (PSMA PET) scans prior to active treatment (radiotherapy or prostatectomy).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>This systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD42023438706). A search was performed on Medline, Embase, Web of Science, and Engineering Village with the following terms: \u2018artificial intelligence\u2019, \u2018prostate cancer\u2019, and \u2018PSMA PET\u2019. All articles published up to February 2024 were considered. Studies were included if patients underwent PSMA PET scan to evaluate intraprostatic lesions prior to active treatment. The two authors independently evaluated titles, abstracts, and full text. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our search yield 948 articles, of which 14 were eligible for inclusion. Eight studies met the primary endpoint of differentiating high\u2010grade PCa. Differentiating between International Society of Urological Pathology (ISUP) Grade Group (GG) \u22653 PCa had an accuracy between 0.671 to 0.992, sensitivity of 0.91, specificity of 0.35. Differentiating ISUP GG \u22654 PCa had an accuracy between 0.83 and 0.88, sensitivity was 0.89, specificity was 0.87. AI could identify non\u2010PSMA\u2010avid lesions with an accuracy of 0.87, specificity of 0.85, and specificity of 0.89. Three studies demonstrated ability of AI to detect extraprostatic extensions with an area under curve between 0.70 and 0.77. Lastly, AI can automate segmentation of intraprostatic lesion and measurement of gross tumour volume.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Although the current state of AI differentiating high\u2010grade PCa is promising, it remains experimental and not ready for routine clinical application. Benefits of using AI to assess intraprostatic lesions on PSMA PET scans include: local staging, identifying otherwise radiologically occult lesions, standardisation and expedite reporting of PSMA PET scans. Larger, prospective, multicentre studies are needed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bju.16412","type":"journal-article","created":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T10:41:38Z","timestamp":1720953698000},"page":"714-722","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A systematic review on artificial intelligence evaluating PSMA PET scan for intraprostatic cancer"],"prefix":"10.1111","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5520-2011","authenticated-orcid":false,"given":"Jianliang","family":"Liu","sequence":"first","affiliation":[{"name":"EJ Whitten Prostate Cancer Research Centre, Epworth Healthcare  Melbourne Victoria Australia"},{"name":"Department of Surgery, The Royal Melbourne Hospital University of Melbourne  Melbourne Victoria Australia"},{"name":"Department of Surgery University of Melbourne  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas P.","family":"Cundy","sequence":"additional","affiliation":[{"name":"Discipline of Surgery University of Adelaide  Adelaide South Australia Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dixon T. 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