Haoyue Zhang

Background/Objectives: Nutraceutical use is common among men with prostate cancer, but its clinical efficacy remains uncertain. We systematically reviewed randomized controlled trials evaluating orally administered nutraceutical and defined dietary interventions after prostate cancer diagnosis. Methods: PubMed, MEDLINE, and Embase were searched through September 2026, and risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Results: Twenty-six trials involving 2188 randomized participants were included. Four were judged to be at low risk of bias, 15 had some concerns, and seven were at high risk. An exploratory random-effects meta-analysis of three soy trials showed no significant difference in post-intervention serum prostate-specific antigen compared with placebo or control (mean difference: −0.50 ng/mL, 95% confidence interval: −2.14 to 1.14; p = 0.55; I² = 0%). Across the wider evidence base, effects on prostate-specific antigen kinetics, tissue biomarkers, oxidative stress, and inflammatory markers were inconsistent. The available evidence was insufficient to establish a validated benefit for disease progression, metastasis, cancer-specific survival, or overall survival. Conclusions: The current evidence is insufficient to establish a clinically meaningful benefit of nutraceutical or defined dietary interventions in prostate cancer management. Larger, well-designed trials using standardized interventions, clinically meaningful endpoints, and longer follow-up are required.

Background/Objectives: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a significant predictor of distal metastases and mortality in prostate cancer (PCa) patients. This systematic review aims to evaluate the accuracy of artificial intelligence (AI) in predicting BCR post-RP. Methods: Adhering to PRISMA guidelines, a comprehensive literature search was conducted across Medline, Embase, Web of Science, and IEEE Xplore. Studies were included if they utilised AI to predict BCR in patients post-RP. Studies involving patients who underwent radiotherapy or salvage RP were excluded. This systematic review was registered on PROSPERO (International prospective register of systematic reviews) under the ID CRD42023482392. Results: After screening 9764 articles, 24 met the inclusion criteria. The included studies involved 27,216 patients, of whom 7267 developed BCR. AI algorithms developed using radiological parameters demonstrated higher predictive accuracy (median AUROC of 0.90) compared to algorithms based solely on pathological variables (median AUROC of 0.74) or clinicopathological variables (median AUROC of 0.81). According to the Prediction Model Risk of Bias Assessment Tool (PROBAST), the overall risk of bias was unclear in three studies due to ambiguous inclusion criteria and the exclusion of many patients because of missing follow-up data. In seven studies, the developed AI outperformed or was at least equivocal to traditional methods of BCR prediction. Conclusions: AI shows promise in predicting BCR post-RP, particularly when radiological data were used in its development. However, the significant variability in AI performance and study methodologies highlights the need for larger, standardised prospective studies with external validation prior to clinical application.