From associations to clinical practice: translating inflammatory-nutritional indices into a machine learning-driven model for breast cancer risk stratification with cross-ethnic validation
TL;DR
The ML model demonstrates good predictive performance, but cross-ethnic validation highlights the need for population-specific calibration, which indicated the potential of ML approaches leveraging inflammatory-nutritional indices to enhance BC risk stratification and inform clinical decision-making.
Abstract
Objectives To evaluate inflammatory-nutritional indices in relation to breast cancer (BC) risk and mortality and develop a cross-ethnically validated prediction model. Methods From National Health and Nutrition Examination Survey (NHANES) 2005–2018, 485 BC patients and 16,838 female controls were included, with mortality follow-up through 2019. Weighted multivariate logistic and Cox regression assessed associations between seven inflammatory indices, two composite indicators, and BC risk/mortality. Multiple machine learning (ML) algorithms, including XGBoost, were used to construct risk models. The model was externally validated (NHANES other periods:1999-2004) and cross-ethnic validated. We prospectively enrolled Chinese treatment-naïve breast cancer patients and matched healthy controls for external validation. Results In fully adjusted models, the Advanced Lung Cancer Inflammation Index (ALI) was inversely associated with BC risk and all-cause mortality (highest vs. lowest tertile: odds ratio [OR] 0.64, 95% CI 0.45–0.91; hazard ratio [HR] 0.41, 95% CI 0.18–0.90). Conversely, neutrophil percentage-to-albumin ratio (NPAR), systemic inflammation response index (SIRI), and neutrophil-to-lymphocyte ratio (NLR) showed positive associations. ALI outperformed other indices in predicting mortality. XGBoost identified NPAR as the top predictive feature; the model incorporating inflammatory indices and age achieved an AUC of 0.832 on the test set, and a web-based dynamic nomogram incorporating these factors was developed. External validation yielded AUCs of 0.781 (NHANES) and 0.730 (Chinese cohort). Conclusions ALI (protective) and NPAR/SIRI/NLR (detrimental) are robust predictors of BC risk and mortality. The ML model demonstrates good predictive performance, but cross-ethnic validation highlights the need for population-specific calibration, which indicated the potential of ML approaches leveraging inflammatory-nutritional indices to enhance BC risk stratification and inform clinical decision-making.