A machine learning approach to predict progression on active surveillance for prostate cancer
Westover, Brandon M
Feldman, Adam S
Faculty AdvisorAdam Feldman
UMass Chan AffiliationsT.H. Chan School of Medicine
Document TypeJournal Article
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AbstractPurpose: 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.
SourceNayan M, Salari K, Bozzo A, Ganglberger W, Lu G, Carvalho F, Gusev A, Schneider A, Westover BM, Feldman AS. A machine learning approach to predict progression on active surveillance for prostate cancer. Urol Oncol. 2022 Apr;40(4):161.e1-161.e7. doi: 10.1016/j.urolonc.2021.08.007. Epub 2021 Aug 29. PMID: 34465541; PMCID: PMC8882704.
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/51250
NotesAndrew Gusev participated in this study as a medical student in the Senior Scholars research program at the UMass Medical School.
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