Development and validation of a machine learning-based prediction model for anemia risk in patients with primary lung cancer receiving platinum-based chemotherapy: a multicenter study.

Background: Anemia represents a frequent and clinically burdensome complication in patients with primary lung cancer receiving platinum-based regimens, yet robust risk stratification tools that integrate multidimensional clinical data and undergo external validation remain scarce. This multicenter study sought to develop and externally validate a machine learning-based predictive model for CIA in this population. Methods: A total of 3,434 patients with primary lung cancer were retrospectively enrolled from two geographically distinct institutions, with 3,097 assigned to the training/internal validation set (7:3 split) and 337 retained as an independent external validation set. Variables with missing rates exceeding 30% were excluded, and remaining missing data were imputed using multiple imputation by chained equations (MICE). Thirteen candidate predictors were preselected via univariate screening and least absolute shrinkage and selection operator (LASSO) regression. Nine machine learning algorithms were developed and comparatively evaluated using area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis. Model interpretability was achieved through SHapley Additive exPlanations (SHAP). The primary outcome was defined as hemoglobin levels < 100 g/L (CTCAE Grade ≥ 2) recorded during the first two cycles of chemotherapy. Results: Among the nine candidate models, LightGBM achieved the highest discriminative performance, with AUCs of 0.871 (95% CI: 0.852-0.891) in the training set, 0.797 (95% CI: 0.763-0.831) in internal validation, and 0.684 (95% CI: 0.626-0.742) in the external validation set derived from Linfen Central Hospital. Calibration and decision curve analyses consistently demonstrated superior agreement and net clinical benefit for LightGBM across all three datasets. SHAP-based interpretation identified lymphocyte count, D-dimer, alanine aminotransferase, total bilirubin, sex, and albumin as the six most influential predictors, with D-dimer, female sex, and outpatient admission positively associated with anemia risk, whereas lymphocyte count, liver function indices, and albumin showed inverse associations. Conclusions: The LightGBM-based model developed herein demonstrates acceptable and moderate predictive performance, satisfactory calibration, and potential clinical utility across geographically diverse populations, offering a preliminary risk-stratification tool for early identification of lung cancer patients at elevated anemia risk prior to chemotherapy initiation. However, given the modest external validation performance, cautious interpretation and further refinement are warranted before widespread clinical deployment.