Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non‐Small Cell Lung Cancer Without Available Targeted Mutations

ABSTRACT Background Non‐small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains the standard therapy for advanced NSCLC without mutations, but drug resistance often reduces effectiveness. Developing more effective methods to predict and monitor chemotherapy benefits early is crucial. Methods We carried out a retrospective cohort study of NSCLC patients without targeted mutations who received chemotherapy at West China Hospital from 2009 to 2013. We identified variables associated with chemotherapy outcomes and built four predictive models by machine learning. Shapley additive explanations (SHAP) interpreted the best model's predictions. The Kaplan–Meier method assessed key variables' impact on 5‐year overall survival. Results The study enrolled 461 NSCLC patients. Eight variables were selected for the model: differentiation, surgery history, neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), total bilirubin (TBIL), total protein (TP), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH). The extreme gradient boosting (Xgboost) model exhibited superior discriminatory ability in predicting complete response (CR) probabilities to chemotherapy, with an AUC of 0.78. SHAP plots showed surgery history and high differentiation were related to CR benefits from chemotherapy. Absence of surgery, higher NLR, higher PLR, and higher LDH were all independent prognostic factors for poor survivals in NSCLC patients without mutations receiving chemotherapy. Conclusions By machine learning, we developed a predictive model to assess chemotherapy benefits in NSCLC patients without targeted mutations, utilizing eight readily available and non‐invasive clinical indicators. Demonstrating satisfactory predictive performance and clinical practicability, this model may help clinicians identify patients' tendency to benefit from chemotherapy, potentially improving their prognosis. In this retrospective cohort study, four machine learning models using readily available clinical features were developed to predict chemotherapy outcomes for NSCLC patients without targeted mutations. The Xgboost model, with its strong predictive performance and clinical utility, may assist clinicians in early identification of patients likely to benefit from chemotherapy, potentially improving their prognosis.