• Department of Urology, Mater Hospital, Brisbane, QLD, Australia. marlonlperera@gmail.com.
• Maxwell Plus, Brisbane, QLD, Australia.
• Department of Surgery, Austin Health, The University of Melbourne, Melbourne, VIC, Australia.
• Department of Urology, Mater Hospital, Brisbane, QLD, Australia.

CONTEXT: The majority of prostate cancer diagnoses are facilitated by testing serum Prostate Specific Antigen (PSA) levels. Despite this, there are limitations to the diagnostic accuracy of PSA. Consideration of patient demographic factors and biochemical adjuncts to PSA may improve prostate cancer risk stratification. We aimed to develop a contemporary, accurate and cost-effective model based on objective measures to improve the accuracy of prostate cancer risk stratification.

METHODS: Data were collated from a local institution and combined with patient data retrieved from the Prostate, Lung, Colorectal and Ovarian Cancer screening Trial (PLCO) database. Using a dataset of 4548 patients, a machine learning model was developed and trained using PSA, free-PSA, age and free-PSA to total PSA (FTR) ratio.

RESULTS: The model was trained on a dataset involving 3638 patients and was then tested on a separate set of 910 patients. The model improved prediction for prostate cancer (AUC 0.72) compared to PSA alone (AUC 0.63), age (AUC 0.52), free-PSA (AUC 0.50) and FTR alone (AUC 0.65). When an operating point is chosen such that the sensitivity of the model is 80% the specificity of the model is 45.3%. The benefit in AUC secondary to the model was related to sample size, with AUC of 0.64 observed when a subset of the cohort was assessed.

CONCLUSIONS: Development of a dense neural network model improved the diagnostic accuracy in screening for prostate cancer. These results demonstrate an additional utility of machine learning methods in prostate cancer risk stratification when using biochemical parameters.