Development and validation of an imageless machine-learning algorithm for the initial screening of prostate cancer.

Personalizing the interpretation of PSA values and DRE results with a gradient-boosting model showed promising results as a potential novel, low-cost method for the initial screening of PCa. The importance of DRE, when included in such a model, was also highlighted.