Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy

Abstract

Biochemical recurrence (BCR) of prostate cancer occurs when the PSA level increases after treatment. BCR prediction is necessary for successful prostate cancer treatment. We propose a model to predict the BCR of prostate cancer using a partial correlation neural network (PCNN). Our study used data from 1021 patients with prostate cancer who underwent radical prostatectomy at a tertiary hospital. There were nine input variables with BCR as the outcome variable. Feature-sensitive and partial correlation analyses were performed to develop the PCNN. The PCNN provides an NN architecture that is optimized for BCR prediction. The proposed PCNN achieved higher performance in BCR prediction than other machine learning methodologies, with accuracy, sensitivity, and specificity values of 87.16%, 90.80%, and 85.62%, respectively. The enhanced performance of the PCNN is owing to the reduction in unnecessary predictive factors through the correlation between the variables that are used. The PCNN can be used in the clinical treatment stage following prostate treatment. It is expected to be used as a clinical decision-making system in clinical follow-ups for prostate cancer.

Keywords

• prostate cancer
• biochemical recurrence
• partial correlation analysis
• neural network