High-risk biochemical recurrence in prostate cancer: identification and early intervention strategies

前列腺癌高危生化复发:识别和早期干预策略

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Abstract

PURPOSE: Biochemical recurrence (BCR) following primary therapy for prostate cancer (PCa) is associated with disease progression; thus, identifying patients at high risk and implementing management strategies remains critical. This expert opinion outlines a set of recommendations for identifying high-risk BCR patients, provides insights into the impact of a multidisciplinary team (MDT) approach on disease management, explores associated costs and resource utilization, and examines the role of androgen receptor signaling pathway inhibitors (ARPIs) in optimizing outcomes. METHODS: The latest evidence and clinical guidelines on risk stratification, diagnostic tools, and collaborative management strategies were evaluated. Additionally, expert opinions were collected from nine oncology and urology experts, and their insights were integrated to form a comprehensive approach tailored for clinical application. RESULTS: The panelists reached agreement on several proposed questions, including patients' early detection, risk stratification, early management, and the role of ARPIs and androgen deprivation therapies (ADT). The recommendations emphasize the need for standardized identification of high-risk BCR patients, treatment protocols, and early intervention strategies. Additionally, the multidisciplinary approach facilitates personalized treatment planning, leveraging various specialties' expertise, and addresses the complexity of resource utilization and cost management. However, a lack of agreement on other topics was observed, such as optimal timing of intervention and resource allocation strategies. CONCLUSION: This narrative, evidence-supported expert-opinion review highlights the importance of standardized protocols, multidisciplinary strategies, and the integration of advanced diagnostics and androgen receptor pathway inhibitors to improve patient outcomes. Further research is warranted to refine predictive models, optimize resource allocation, and enhance therapeutic efficacy.

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