Predicting survival after radical prostatectomy: Variation of machine learning performance by race. Issue 16 (16th September 2021) - Nayan, Madhur; Salari, Keyan; Bozzo, Anthony; Ganglberger, Wolfgang; Carvalho, Filipe; Feldman, Adam S.; Trinh, Quoc‐Dien
Abstract: Background: Robust prediction of survival can facilitate clinical decision‐making and patient counselling. Non‐Caucasian males are underrepresented in most prostate cancer databases. We evaluated the variation in performance of a machine learning (ML) algorithm trained to predict survival after radical prostatectomy in race subgroups. Methods: We used the National Cancer Database (NCDB) to identify patients undergoing radical prostatectomy between 2004 and 2016. We grouped patients by race into Caucasian, African‐American, or non‐Caucasian, non‐African‐American (NCNAA) subgroups. We trained an Extreme Gradient Boosting (XGBoost) classifier to predict 5‐year survival in different training samples: naturally race‐imbalanced, race‐specific, and synthetically race‐balanced. We evaluated performance in the test sets. Results: A total of 68, 630 patients met inclusion criteria. Of these, 57, 635 (84%) were Caucasian, 8173 (12%) were African‐American, and 2822 (4%) were NCNAA. For the classifier trained in the naturally race‐imbalanced sample, the F1 scores were 0.514 (95% confidence interval: 0.513–0.511), 0.511 (0.511–0.512), 0.545 (0.541–0.548), and 0.378 (0.378–0.389) in the race‐imbalanced, Caucasian, African‐American, and NCNAA test samples, respectively. For all race subgroups, the F1 scores of classifiers trained in the race‐specific or synthetically race‐balanced samples demonstrated similar performance compared to training in the naturally race‐imbalanced sample. Conclusions: A ML algorithm trained using NCDB data to predict survival after radical prostatectomy demonstrates variation in performance by race, regardless of whether the algorithm is trained in a naturally race‐imbalanced, race‐specific, or synthetically race‐balanced sample. These results emphasize the importance of thoroughly evaluating ML algorithms in race subgroups before clinical deployment to avoid potential disparities in care.