A machine learning approach to predict progression on active surveillance for prostate cancer.

ABSTRACT:

Purpose

Robust prediction of progression on active surveillance (AS) for prostate cancer can allow for risk-adapted protocols. To date, models predicting progression on AS have invariably used traditional statistical approaches. We sought to evaluate whether a machine learning (ML) approach could improve prediction of progression on AS.

Patients and methods

We performed a retrospective cohort study of patients diagnosed with very-low or low-risk prostate cancer between 1997 and 2016 and managed with AS at our institution. In the training set, we trained a traditional logistic regression (T-LR) classifier, and alternate ML classifiers (support vector machine, random forest, a fully connected artificial neural network, and ML-LR) to predict grade-progression. We evaluated mode

SUBMITTER: Nayan M 

PROVIDER: S-EPMC8882704 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature