Deep Neural Networks Outperform the CAPRA Score in Predicting Biochemical Recurrence After Prostatectomy.

ABSTRACT:

Background

Use of predictive models for the prediction of biochemical recurrence (BCR) is gaining attention for prostate cancer (PCa). Specifically, BCR occurs in approximately 20-40% of patients five years after radical prostatectomy (RP) and the ability to predict BCR may help clinicians to make better treatment decisions. We aim to investigate the accuracy of CAPRA score compared to others models in predicting the 3-year BCR of PCa patients.

Material and methods

A total of 5043 men who underwent RP were analyzed retrospectively. The accuracy of CAPRA score, Cox regression analysis, logistic regression, K-nearest neighbor (KNN), random forest (RF) and a densely connected feed-forward neural network (DNN) classifier were compared in terms of 3-year BCR predictive value. The

SUBMITTER: Sargos P 

PROVIDER: S-EPMC7906005 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature