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From associations to clinical practice: translating inflammatory-nutritional indices into a machine learning-driven
Yue Li1, Ting Ding1, Xiaoyan Zhou1
1Department of Clinical Laboratories, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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.