Urol Oncol. 2021 Aug 28:S1078-1439(21)00366-5. doi: 10.1016/j.urolonc.2021.08.007. Online ahead of print.
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 model performance in the test set. The primary performance metric was the F1 score.
RESULTS: Our cohort included 790 patients. With a median follow-up of 6.29 years, 234 developed grade-progression. In descending order, the F1 scores were: support vector machine 0.586 (95% CI 0.579 – 0.591), ML-LR 0.522 (95% CI 0.513 – 0.526), artificial neural network 0.392 (95% CI 0.379 – 0.396), random forest 0.376 (95% CI 0.364 – 0.380), and T-LR 0.182 (95% CI 0.151 – 0.185). All alternate ML models had a significantly higher F1 score than the T-LR model (all p <0.001).
CONCLUSION: In our study, ML methods significantly outperformed T-LR in predicting progression on AS for prostate cancer. While our specific models require further validation, we anticipate that a ML approach will help produce robust prediction models that will facilitate individualized risk-stratification in prostate cancer AS.
PMID:34465541 | DOI:10.1016/j.urolonc.2021.08.007
