Artificial intelligence and multi-omics integration in liquid biopsy for genitourinary cancers: a systematic scoping review.

backgroundLiquid biopsy, combined with multi-omics profiling and artificial intelligence (AI), offers a minimally invasive strategy for cancer detection, monitoring, and prognostication. Evidence in genitourinary (GU) cancers remains fragmented across analytes, biofluids, and computational approaches, limiting clinical translation.

objectiveTo map and synthesize AI-enabled liquid biopsy research in GU cancers and identify methodological and clinical gaps.

methodsA systematic scoping review was conducted following PRISMA-ScR and JBI guidelines (protocol registered in PROSPERO: CRD420251137304). Comprehensive searches of MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, IEEE Xplore, preprint servers, and major conference proceedings identified eligible studies published 1 January 2018-1 August 2025. Dual independent screening and data charting were performed. Note on performance metrics: reported AUC, sensitivity, and specificity values were extracted verbatim from included studies and from previously published meta-analyses when cited; this review did not perform a de-novo quantitative meta-analysis or statistical pooling of primary study results.

resultsA total of 115 studies were included, spanning biomarker discovery, algorithm development, and early translational efforts. Urinary exosome diagnostics in urothelial cancer have been reported in prior meta-analyses to show pooled AUC ≈ 0.83 (sensitivity ≈ 75%, specificity ≈ 77%); these pooled estimates were cited but not recalculated in the present scoping review. ctDNA-based approaches showed strong prognostic value in advanced prostate cancer, with detection rates consistently > 60%. Testicular germ cell tumor research remains dominated by miR-371a-3p studies, with limited AI or multi-omics integration. Intermediate-fusion and graph-based models offered advantages over simple concatenation but were hindered by small sample sizes, batch effects, and lack of external validation.

conclusionAI-driven multi-omics liquid biopsy holds promise for GU oncology but is constrained by fragmented evidence and methodological variability. Future research should prioritize standardized pipelines, multi-center validation, prospective trials, and explainable AI to accelerate clinical translation.