Personalized Biopsy Schedules Using an Interval-Censored Cause-Specific Joint Model.

Area of Science:

• Oncology
• Biostatistics
• Medical Informatics

Background:

• Active surveillance (AS) for prostate cancer reduces overtreatment but relies on fixed biopsy schedules.
• Current fixed biopsy schedules are not personalized and can lead to patient burden from frequent invasive procedures.
• The optimal frequency for monitoring cancer progression in AS remains undetermined.

Purpose of the Study:

• To develop a personalized biopsy scheduling strategy for prostate cancer active surveillance.
• To model the impact of longitudinal biomarkers on cancer progression while accounting for competing risks.
• To optimize biopsy timing by balancing the number of procedures and detection delay.

Main Methods:

• Proposed an interval-censored cause-specific joint model (ICJM) to integrate longitudinal biomarkers and cancer progression.
• Incorporated interval-censoring, competing risks of early treatment, and uncertainty in progression detection.
• Developed patient-specific risk profiles to trigger biopsies when a predefined risk threshold is exceeded.

Main Results:

• The ICJM successfully models cancer progression with competing risks and interval-censoring.
• Personalized biopsy schedules significantly reduced the number of biopsies per patient by 41%-52% compared to fixed schedules.
• This reduction in biopsies came with a slight increase in the delay of cancer progression detection.

Conclusions:

• Personalized biopsy schedules based on patient-specific risk profiles are feasible and effective for prostate cancer AS.
• The proposed ICJM offers a robust statistical framework for optimizing AS monitoring.
• This approach has the potential to reduce patient burden and healthcare costs associated with prostate cancer management.