Personalized schedules for surveillance of low-risk prostate cancer patients

Summary. Low-risk prostate cancer patients enrolled in active surveillance (AS) programs commonly undergo biopsies on a frequent basis for examination of cancer progression. AS programs employ a fixed schedule of biopsies for all patients. Such fixed and frequent schedules may schedule unnecessary biopsies. Since biopsies are burdensome, patients do not always comply with the schedule, which increases the risk of delayed detection of cancer progression. Motivated by the world’s largest AS program, Prostate Cancer Research International Active Surveillance (PRIAS), we present personalized schedules for biopsies to counter these problems. Using joint models for time-to-event and longitudinal data, our methods combine information from historical prostate-specific antigen levels and repeat biopsy results of a patient, to schedule the next biopsy. We also present methods to compare personalized schedules with existing biopsy schedules

Personalized Schedules for Invasive Diagnostic Tests: With Applications in Surveillance of Chronic Non-Communicable Diseases

Benchmark surveillance tests for diagnosing disease progression (biopsies, endoscopies, etc.) in early-stage chronic non-communicable disease patients (e.g., cancer, lung diseases) are usually invasive. For detecting progression timely, over their lifetime, patients undergo numerous invasive tests planned in a fixed one-size-fits-all manner (e.g., biannually). We present personalized test schedules based on progression-risk, that aim to optimize the number of tests (burden) and time delay in detecting progression (shorter is beneficial) better than fixed schedules. Our motivation comes from the problem of scheduling biopsies in prostate cancer surveillance studies. Using joint models for time-to-event and longitudinal data, we consolidate patients’ longitudinal data (e.g., biomarkers) and results of previous tests, into individualized future cumulative-risk of progression. We then create personalized schedules by planning tests on future visits where the predicted cumulative-risk is above a particular threshold (e.g., 5% risk). This schedule is updated on each follow-up with newly gathered data. To find the optimal risk threshold, we minimize a utility function of the expected number of tests (burden) and expected time delay in detecting progression (shorter is beneficial) for different thresholds. We estimate these two quantities in a patient-specific manner for following any schedule by utilizing the predicted risk profile of the patient. Patients/doctors can employ these quantities to compare various personalized and fixed schedules objectively. Last, we implement our methodology in a web-application for prostate cancer patients