Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026
Performance of Statistical and Machine Learning Risk Prediction Models for Advanced Breast Cancer.
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Chen Shuai, Kerlikowske Karla, Su Yu-Ru, Hubbard Rebecca A, Gard Charlotte C, Tice Jeffrey A, Sprague Brian L, Ahern Thomas P, Miglioretti Diana L
Аннотация
BACKGROUND: Machine learning enables complex risk prediction models, but comparative performance with statistical approaches remains context-dependent. We compared statistical and machine learning models for predicting advanced breast cancer risk.
METHODS: Using data from 968,178 women (40-74 years) undergoing 2,796,459 annual or 812,126 biennial screening mammograms (2005-2019) in the Breast Cancer Surveillance Consortium, we cross-validated models predicting advanced breast cancer within 12 months (annual) or 24 months (biennial) following screening. Models included conventional logistic regression, regularized regressions [least absolute shrinkage and selection operator (LASSO), elastic net], and machine learning methods (random forests, gradient boosting), considering a modest number of clinical and demographic predictors. Performance was assessed using calibration and area under the receiver operating characteristic curve (AUC).
RESULTS: Discrimination was similar across models (AUC 0.677-0.690). Calibration differences were more pronounced. Regularized regressions achieved the most favorable calibration overall and across racial and ethnic groups, with an AUC of 0.689 [95% confidence interval (CI), 0.676-0.701]. Gradient boosting showed a comparable AUC but suboptimal calibration (calibration slope 1.12; 95% CI, 1.04-1.20). Conventional logistic regression had a slightly lower AUC (0.683; 95% CI, 0.671-0.696) and a calibration slope of 0.90 (95% CI, 0.83-0.96). Regression-based approaches were generally well calibrated across racial and ethnic groups (expected-to-observed event ratio 0.96-1.03; calibration intercept -0.03 to 0.04), with some subgroup deviations in calibration slopes (<1).
CONCLUSIONS: For predicting advanced breast cancers, regularized regression demonstrated similar discrimination and generally more favorable calibration than other approaches.
IMPACT: In settings with rare outcomes and low-dimensional features, regularized regression may offer a practical balance between performance and interpretability.
METHODS: Using data from 968,178 women (40-74 years) undergoing 2,796,459 annual or 812,126 biennial screening mammograms (2005-2019) in the Breast Cancer Surveillance Consortium, we cross-validated models predicting advanced breast cancer within 12 months (annual) or 24 months (biennial) following screening. Models included conventional logistic regression, regularized regressions [least absolute shrinkage and selection operator (LASSO), elastic net], and machine learning methods (random forests, gradient boosting), considering a modest number of clinical and demographic predictors. Performance was assessed using calibration and area under the receiver operating characteristic curve (AUC).
RESULTS: Discrimination was similar across models (AUC 0.677-0.690). Calibration differences were more pronounced. Regularized regressions achieved the most favorable calibration overall and across racial and ethnic groups, with an AUC of 0.689 [95% confidence interval (CI), 0.676-0.701]. Gradient boosting showed a comparable AUC but suboptimal calibration (calibration slope 1.12; 95% CI, 1.04-1.20). Conventional logistic regression had a slightly lower AUC (0.683; 95% CI, 0.671-0.696) and a calibration slope of 0.90 (95% CI, 0.83-0.96). Regression-based approaches were generally well calibrated across racial and ethnic groups (expected-to-observed event ratio 0.96-1.03; calibration intercept -0.03 to 0.04), with some subgroup deviations in calibration slopes (<1).
CONCLUSIONS: For predicting advanced breast cancers, regularized regression demonstrated similar discrimination and generally more favorable calibration than other approaches.
IMPACT: In settings with rare outcomes and low-dimensional features, regularized regression may offer a practical balance between performance and interpretability.