To develop and validate a risk calculator for prostate cancer (PC) and clinically significant PC (csPC) using explainable artificial intelligence (XAI).
We used data of 3791 patients to develop and validate the risk calculator. We initially divided the data into development and validation sets. An extreme gradient-boosting algorithm was applied to the development calculator using five-fold cross-validation with hyperparameter tuning following feature selection in the development set. The model feature importance was determined based on the Shapley value. The area under the curve (AUC) of the receiver operating characteristic curve was analysed for each validation set of the calculator.
Approximately 1216 (32.7%) and 562 (14.8%) patients were diagnosed with PC and csPC. The data of 2843 patients were used for development, whereas the data of 948 patients were used as a test set. We selected the variables for each PC and csPC risk calculation according to the least absolute shrinkage and selection operator regression. The AUC of the final PC model was 0.869 (95% confidence interval (CI); 0.844 to 0.893), whereas that of the csPC model was 0.945 (95% CI; 0.927 to 0.963). The prostate-specific antigen (PSA), free PSA, age, prostate volume (both the transitional zone and total), hypoechoic lesions on ultrasound, and testosterone level were found to be important parameters in the PC model. The number of previous biopsies was not associated with the risk of csPC, but was negatively associated with the risk of PC.
We successfully developed and validated a decision-supporting tool using XAI for calculating the probability of PC and csPC prior to prostate biopsy.

This article is protected by copyright. All rights reserved.

References

PubMed