Salvage treatment strategies for recurrence after radical prostatectomy: Current status, challenges, and future directions

Abstract

Objective Recurrence after radical prostatectomy (RP) remains a major clinical challenge in prostate cancer and is characterized by substantial heterogeneity in recurrence patterns. This review aimed to summarize current evaluation strategies, recent advances in salvage treatment, and future directions for patients with recurrence after RP. Methods We conducted a structured narrative review of PubMed/MEDLINE, Embase, Web of Science Core Collection, and the Cochrane Library from database inception to 20 August 2026. Studies were selected using prespecified eligibility criteria and synthesized by recurrence state and treatment modality, with postoperative evidence distinguished from extrapolated, early-phase, and preclinical evidence. Results Emerging imaging modalities have improved the detection, localization, and risk stratification of recurrence after RP. Early salvage radiotherapy remains the principal potentially curative treatment for localized recurrence, with androgen-deprivation therapy considered selectively according to adverse-risk features. In oligometastatic recurrence, small randomized phase II trials and retrospective studies suggest that metastasis-directed therapy may delay progression or the initiation of systemic therapy in selected patients, although an overall-survival benefit has not been established. In metastatic or castration-resistant disease, androgen receptor pathway inhibitors, poly(ADP-ribose) polymerase inhibitors, and PSMA-targeted radioligand therapy such as [ 177 Lu]Lu-PSMA-617 have improved outcomes in appropriately selected advanced-disease populations. Conclusion Salvage treatment for recurrence after RP has shifted from a one-size-fits-all approach to a precision-based, individualized strategy guided by multimodal information. Future research should further emphasize artificial intelligence-assisted decision-making, targeted intervention based on molecular subtyping, and the development of integrated multiparametric predictive models.