MRI-derived radiomics for prostate cancer active surveillance: a systematic review.

PURPOSE: To systematically review MRI-derived radiomic and artificial intelligence models predicting outcomes in men on or eligible for active surveillance (AS) for prostate cancer, and whether reported endpoints permit quantitative synthesis.

METHOD: Following PRISMA 2020, we searched PubMed, Scopus, Embase, and Web of Science (2000-September 2026). Two reviewers independently screened, extracted data, and assessed risk of bias (QUADAS-2, PROBAST) and methodological quality (METRICS). Three subgroups were defined: AS selection at diagnosis, baseline detection of clinically significant cancer, and progression during follow-up. Pooling was pre-specified for subgroups with at least four independent cohorts; endpoint equivalence was assessed.

RESULTS

Seventeen studies across 12 unique cohorts were included. Seven publications derived from two cohorts; 12 independent cohort-level estimates (n = 2703) remained after overlap resolution. Nine publications (four independent cohorts) addressed progression (AUC 0.70 to 0.95), but progression was defined in five non-equivalent ways, from any grade-group increase to composite core-count criteria, precluding quantitative synthesis. Risk of bias was high or unclear in 16 of 17 studies; the median METRICS score was 32.3 % (range 18.9-52.3 %). No study reported external validation or calibration for progression.

CONCLUSIONS

Progression endpoints were heterogeneous and several were of limited clinical relevance. A core outcome set for AS (at least ISUP grade group ≥3 and/or unequivocal radiological progression), followed by multi-centre external validation with a head-to-head comparison against the PRECISE v2 (Prostate Cancer Radiological Estimation of Change in Sequential Evaluation) score, is the priority before clinical use can be considered.