Prostate Cancer Diagnosis from Structured Clinical Biomarkers with Deep Learning

Prostate cancer (PC) is one of the most aggressive cancers that exist. Early detection of PC is indispensable for treatment. Biopsies are often carried out to determine the Gleason score of PC which helps to predict the aggressiveness of PC. As biopsies have considerable associated risk, especially for old people, machine learning can be used to predict the PC Gleason grade from clinical biomarkers. These biomarkers are typically structured in a table. In this paper, we propose to use advanced tabular deep neural network architectures, like TabNet and TabTransformer, to grade PC. We also perform a comparative study of various machine learning approaches, including traditional methods, tree-based classifiers, and shallow neural networks, for this purpose. Our experimental results demonstrate the superior performance of the TabNet deep learning method.