A preliminary study of a machine-learning prediction model for lung cancer diagnosis based on routine blood indicators and traditional chinese medicine diagnostic parameters.

Background: Early identification of lung cancer remains challenging in clinical practice, especially in patients who cannot undergo advanced imaging or invasive examinations. Traditional Chinese Medicine (TCM) provides distinctive diagnostic insights, but the potential role of its objective features in tumor screening remains insufficiently investigated. The objective of this study was to establish a multimodal machine-learning model combining routine laboratory examinations with selected objectified TCM indicators to screen for lung cancer. Methods: This real-world, retrospective cohort study included patients who visited the Departments of Oncology and General Surgery at Longhua Hospital, Shanghai University of Traditional Chinese Medicine, between January 2022 and December 2023. Following inclusion and exclusion screening, a total of 309 patients with pathologically confirmed lung cancer and 205 non-cancer patients were enrolled. All participants were randomly assigned using computer-generated randomisation in a 70:30 ratio to a training cohort (n = 358) and a validation cohort (n = 156). Five machine-learning algorithms and a logistic regression (LR) model were trained in the training cohort. Model performance in the validation cohort was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. SHAP analysis was applied to interpret feature contributions. Results: LASSO identified 13 key variables, encompassing age, blood biochemical indicators, objective tongue features, and TCM constitution scores. The XGBoost model constructed based on these variables demonstrated good discriminative performance for lung cancer in the validation set (AUC = 0.8745, 95% CI: 0.8219-0.9271; F1 = 0.7711). SHAP analysis revealed that age, absolute lymphocyte count, Qi-Deficiency Constitution Score (QDC-Score), apolipoprotein C3 (ApoC3), and tongue color features, including tongue coating color L* (TC-L) and full tongue color b* (FT-B), were the key factors driving model predictions. Conclusion: We developed a low-cost screening model that integrates TCM-related features with routine clinical parameters, demonstrating good accuracy for identifying high-risk individuals for lung cancer among non-tumor populations. This multimodal approach highlights the complementary value of integrating TCM indices with biomedical variables. Prospective multicenter validation is required to further optimize this model and facilitate its implementation in real-world clinical practice.