{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T03:45:44Z","timestamp":1791776744728,"version":"4.3.4"},"reference-count":39,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJU International"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To systematically identify, appraise and synthesise artificial intelligence (AI) and machine\u2010learning (ML) models that predict treatment response and clinical outcomes after intravesical bacillus Calmette\u2013Gu\u00e9rin (BCG) in non\u2010muscle\u2010invasive bladder cancer (NMIBC), a setting in which current risk calculators underperform, and identifying non\u2010responders has become urgent as alternatives to BCG enter practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>PubMed, EMBASE and Web of Science were searched through April 2026 following the Preferred Reporting Items for Systematic Reviews and Meta\u2010Analyses (PRISMA) 2020 guidelines (International Prospective Register of Systematic Reviews [PROSPERO] number CRD420261376808). Studies developing or validating AI\/ML models for BCG\u2010associated outcomes with a quantitative performance metric were included. Risk of bias was assessed with the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Evidence was synthesised along a clinically oriented framework: AI as a perceptual tool (extracting signal from histology or imaging) or integrative tool (re\u2010weighting clinicopathological, molecular, or urinary variables).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 15 studies (&gt;24\u2009900 patients) were included: seven perceptual, eight integrative. By input data, six used digital pathology, two radiomics, two genomics\/transcriptomics, four clinicopathological markers, and one urinary biomarkers. The digital\u2010pathology Computational Histology Artificial Intelligence (CHAI) platform, validated across 12 international centres, stratified high\u2010grade recurrence (hazard ratio [HR] 2.08), progression (HR 3.87) and BCG\u2010unresponsive disease (HR 2.31), and was the only model providing a first signal of predictive value, demonstrating a significant BCG vs gemcitabine\/docetaxel interaction (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.029). Integrative models PROGRxN\u2010BCa (concordance index [C\u2010index] 0.79) and DeepSurv (C\u2010index 0.881) outperformed standard calculators but with modest gains (\u0394C\u2010index 0.05\u20130.10). Only 40% of studies performed external validation, none prospectively; five were at high risk of bias.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Perceptual AI, particularly digital pathology, has the highest external validation and provides the only biomarker with a first signal of predictive value for BCG vs alternatives, increasingly relevant in the context of the global BCG shortage. Integrative models such as PROGRxN\u2010BCa and DeepSurv outperform standard calculators with incremental gains and are freely accessible. Prospective validation, systematic calibration reporting and treatment\u2010by\u2010biomarker interaction analyses, ideally embedded in trials such as the BRIDGE trial (ClinicalTrials.gov identifier: NCT05538663), remain priorities before clinical adoption.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bju.70404","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T08:13:20Z","timestamp":1786954400000},"page":"786-797","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for predicting\n                    <scp>BCG<\/scp>\n                    response in non\u2010muscle\u2010invasive bladder cancer: a systematic review"],"prefix":"10.1111","volume":"138","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0399-2395","authenticated-orcid":false,"given":"Ludovica","family":"Cella","sequence":"first","affiliation":[{"name":"Department of Biomedical Sciences Humanitas University  Milan Italy"},{"name":"Department of Urology IRCCS Humanitas Research Hospital  Milan Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7011-0537","authenticated-orcid":false,"given":"Roberto","family":"Contieri","sequence":"additional","affiliation":[{"name":"Urology Department Istituto Nazionale Tumori di Napoli, IRCCS \u201cG. 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