Personalized Decision Making for Biopsies in Prostate Cancer Active Surveillance Programs.

Background. Low-risk prostate cancer patients enrolled in active surveillance programs commonly undergo biopsies for examination of cancer progression. Biopsies are conducted as per a fixed and frequent schedule (e. g., annual biopsies). Since biopsies are burdensome, patients do not always comply with the schedule, which increases the risk of delayed detection of cancer progression. Objective. Our aim is to better balance the number of biopsies (burden) and the delay in detection of cancer progression (less is beneficial) by personalizing the decision of conducting biopsies. Data Sources. We used patient data of the world's largest active surveillance program (Prostate Cancer Research International Active Surveillance; PRIAS). It enrolled 5270 patients, had 866 cancer progressions, and an average of 9 prostate-specific antigen (PSA) and 5 digital rectal examination (DRE) measurements per patient. Methods. Using joint models for time-to-event and longitudinal data, we model the historical DRE and PSA measurements and biopsy results of a patient at each follow-up visit. This results in a visit and patient-specific cumulative risk of cancer progression. If this risk is above a certain threshold, we schedule a biopsy. We compare this personalized approach with the currently practiced biopsy schedules via an extensive and realistic simulation study, based on a replica of the patients from the PRIAS program. Results. The personalized approach saved a median of 6 biopsies (median: 4, interquartile range [IQR]: 2-5) compared with the annual schedule (median: 10, IQR: 3-10). However, the delay in detection of progression (years) is similar for the personalized (median: 0.7, IQR: 0.3-1.0) and the annual schedule (median: 0.5, IQR: 0.3-0.8). Conclusions. We conclude that personalized schedules provide substantially better balance in the number of biopsies per detected progression for men with low-risk prostate cancer.

Medical decision making : an international journal of the Society for Medical Decision Making. 2019 Jul 18 [Epub ahead of print]

Anirudh Tomer, Dimitris Rizopoulos, Daan Nieboer, Frank-Jan Drost, Monique J Roobol, Ewout W Steyerberg

Department of Biostatistics, Erasmus University Medical Center, Rotterdam, Zuid-Holland, the Netherlands., Department of Public Health, Erasmus University Medical Center, Rotterdam, Zuid-Holland, the Netherlands., Department of Urology, Erasmus University Medical Center, Rotterdam, Zuid-Holland, the Netherlands.

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