Mortality prediction with adjuvant tamoxifen in breast cancer: Machine learning-integrated explainable artificial intelligence and Bayesian model results
Abstract
Background and purpose: Tamoxifen is a cornerstone of adjuvant endocrine therapy for breast cancer, yet significant inter-individual variability in treatment response and mortality exists. Identifying robust predictors of outcomes remains a critical need. This study integrated machine learning, explainable artificial intelligence (XAI) and Bayesian modelling to predict mortality and identify key prognostic factors in breast cancer patients receiving adjuvant tamoxifen.
Experimental approach: We analysed data from 568 patients from the International Tamoxifen Pharmacogenomics Consortium database. The outcome was all-cause mortality, with predictors including age, race, menopausal status, tumour size, estrogen receptor status, radiation treatment, and CYP2D6 metabolizer status. Four algorithms, logistic regression, random forest, eXtreme Gradient Boosting (XGBoost) and support vector machine, were developed and validated. Model performance was assessed using accuracy and area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) analysis provided interpretability for the XGBoost model, and Bayesian logistic regression with weakly informative priors was employed for probabilistic inference.
Key results: The overall mortality rate was 19.4 %. XGBoost demonstrated the highest discriminative ability (AUC 0.833; 95 % confidence interval: 0.725 to 0.941), while random forest exhibited superior sensitivity for identifying deceased patients…
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