Integrating Serum Biomarkers into Prediction Models for Biochemical Recurrence Following Radical Prostatectomy.

Area of Science:

• Urology
• Oncology
• Biochemistry

Background:

• Prostate cancer recurrence after radical prostatectomy is a significant clinical concern.
• Accurate prediction of biochemical recurrence (BCR) is crucial for guiding post-operative management and improving patient outcomes.

Purpose of the Study:

• To develop and validate a predictive model for BCR in prostate cancer patients post-radical prostatectomy.
• To assess the added value of serum biomarkers, specifically PEDF, in conjunction with clinical features for BCR prediction.

Main Methods:

• Utilized three radical prostatectomy cohorts for model development and validation.
• Employed the Cox proportional hazard model with stepwise selection and cross-validation techniques.
• Evaluated model performance using AUC, calibration, and decision curve analysis.

Main Results:

• The integrated model combining clinical variables and serum biomarkers achieved an AUC of 0.695, significantly outperforming clinical variables alone (AUC=0.604) or biomarkers alone (AUC=0.573).
• Validation in independent cohorts showed improved predictive ability (AUCs of 0.724 and 0.606) compared to the clinical model (AUCs of 0.665 and 0.511).
• The pre-operative biomarker PEDF was identified as a key factor in enhancing prediction accuracy.

Conclusions:

• Integrating pre-operative serum biomarker PEDF with clinical factors significantly improves the prediction of BCR after prostatectomy.
• This enhanced predictive model can aid in pre-treatment clinical decision-making, potentially improving patient outcomes and quality of life.