Comparing a new risk prediction model with prostate cancer risk calculator apps in a Taiwanese population

Purpose: To develop a novel Taiwanese prostate cancer (PCa) risk model for predicting PCa, comparing its predictive performance with that of two well-established PCa risk calculator apps.

Methods: 1545 men undergoing prostate biopsies in a Taiwanese tertiary medical center between 2012 and 2019 were identified retrospectively. A five-fold cross-validated logistic regression risk model was created to calculate the probabilities of PCa and high-grade PCa (Gleason score ≧ 7), to compare those of the Rotterdam and Coral apps. Discrimination was analyzed using the area under the receiver operator characteristic curve (AUC). Calibration was graphically evaluated with the goodness-of-fit test. Decision-curve analysis was performed for clinical utility. At different risk thresholds to biopsy, the proportion of biopsies saved versus low- and high-grade PCa missed were presented.

Results: Overall, 278/1309 (21.2%) patients were diagnosed with PCa, and 181 out of 278 (65.1%) patients had high-grade PCa. Both our model and the Rotterdam app demonstrated better discriminative ability than the Coral app for detection of PCa (AUC: 0.795 vs 0.792 vs 0.697, DeLong's method: P < 0.001) and high-grade PCa (AUC: 0.869 vs 0.873 vs 0.767, P < 0.001). Using a ≥ 10% risk threshold for high-grade PCa to biopsy, our model could save 67.2% of total biopsies; among these saved biopsies, only 3.4% high-grade PCa would be missed.

Conclusion: Our new logistic regression model, similar to the Rotterdam app, outperformed the Coral app in the prediction of PCa and high-grade PCa. Additionally, our model could save unnecessary biopsies and avoid missing clinically significant PCa in the Taiwanese population.