Personalised biopsy schedules based on risk of Gleason upgrading for patients with low‐risk prostate cancer on active surveillance

Objective To develop a model and methodology for predicting the risk of Gleason upgrading in patients with prostate cancer on active surveillance (AS) and using the predicted risks to create risk‐based personalised biopsy schedules as an alternative to one‐size‐fits‐all schedules (e.g. annually). Furthermore, to assist patients and doctors in making shared decisions on biopsy schedules, by providing them quantitative estimates of the burden and benefit of opting for personalised vs any other schedule in AS. Lastly, to externally validate our model and implement it along with personalised schedules in a ready to use web‐application. Patients and Methods Repeat prostate‐specific antigen (PSA) measurements, timing and results of previous biopsies, and age at baseline from the world’s largest AS study, Prostate Cancer Research International Active Surveillance (PRIAS; 7813 patients, 1134 experienced upgrading). We fitted a Bayesian joint model for time‐to‐event and longitudinal data to this dataset. We then validated our model externally in the largest six AS cohorts of the Movember Foundation’s third Global Action Plan (GAP3) database (>20 000 patients, 27 centres worldwide). Using the model predicted upgrading risks; we scheduled biopsies whenever a patient’s upgrading risk was above a certain threshold. To assist patients/doctors in the choice of this threshold, and to compare the resulting personalised schedule with currently practiced schedules, along with the timing and the total number of biopsies (burden) planned, for each schedule we provided them with the time delay expected in detecting upgrading (shorter is better). Results The cause‐specific cumulative upgrading risk at the 5‐year follow‐up was 35% in PRIAS, and at most 50% in the GAP3 cohorts. In the PRIAS‐based model, PSA velocity was a stronger predictor of upgrading (hazard ratio [HR] 2.47, 95% confidence interval [CI] 1.93–2.99) than the PSA level (HR 0.99, 95% CI 0.89–1.11). Our model had a moderate area under the receiver operating characteristic curve (0.6–0.7) in the validation cohorts. The prediction error was moderate (0.1–0.2) in theGAP3 cohorts where the impact of the PSA level and velocity on upgrading risk was similar to PRIAS, but large (0.2–0.3) otherwise. Our model required re‐calibration of baseline upgrading risk in the validation cohorts. We implemented the validated models and the methodology for personalised schedules in a web‐application (http://tiny.cc/biopsy). Conclusions