Personalized Dynamic Prediction Model for Biopsy Timing in Patients With Prostate Cancer During Active Surveillance

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Importance: Active surveillance (AS) for patients with prostate cancer (PC) often includes fixed repeat prostate biopsies that do not account for the varying risk of reclassification to significant disease. Given the invasive nature and potential complications of biopsies, a personalized approach is needed to balance the burden of biopsies with the risk of missing disease progression. Objective: To develop and externally validate a dynamic model that predicts an individual's risk of PC reclassification during AS. Design, Setting, and Participants: This prognostic study developed a dynamic prediction model using data from the Prostate Cancer Research International: Active Surveillance (PRIAS) study, which was initiated in 2006. Follow-up was truncated until April 2023. External validation was conducted using cohorts from the world's largest centralized AS database, the Global Action Plan Prostate Cancer Active Surveillance initiative database. The PRIAS study is a multicenter, prospective, web-based cohort study monitoring patients undergoing AS, involving more than 175 academic, nonacademic, and private centers across 23 countries worldwide. For the development and external validation of the model, all patients diagnosed with Grade Group 1 PC who underwent at least 1 baseline or follow-up magnetic resonance imaging (MRI) and 1 follow-up biopsy were included. Data were analyzed from September 2023 to January 2024. Exposures: AS, including prostate-specific antigen (PSA) tests, MRI, and prostate biopsies according to a fixed follow-up schedule. Main Outcomes and Measures: A joint model for longitudinal and time-to-event data was used to predict reclassification to Grade Group 2 or greater on repeat biopsy using predefined baseline and repeated clinical characteristics. Performance was assessed using time-dependent area under the receiver operating characteristic curve and negative predictive value. Results: The development cohort included 2512 patients (median [IQR] age, 65 [59-69] years). Characteristics significantly associated with a higher risk of reclassification were increased age, higher PSA and velocity, lower prostate volume, a suspicious lesion on MRI, and no previous negative biopsy findings. Depending on the threshold and time point used, the model demonstrated a negative predictive value of 86% to 97%. External validation included 3199 patients from 9 other cohorts. The time-dependent area under the curve ranged from 0.81 to 0.84 in the development cohort and 0.52 to 0.90 at external validation. Conclusions and Relevance: In this prognostic study, the developed dynamic risk model effectively identified patients at low risk of PC reclassification during AS. After prospective validation, this model may support personalized, risk-based AS and reduce the burden of unnecessary biopsies.

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