Machine learning-based prediction and external validation of treatment-related myelosuppression in patients with non-small cell lung cancer receiving PD-1 inhibitors plus platinum-doublet chemotherapy.

Background: The application of programmed cell death protein 1 (PD-1) inhibitors combined with platinum-based double-drug chemotherapy in patients with non-small cell lung cancer (NSCLC) is becoming increasingly widespread. However, the impact of this treatment regimen on the bone marrow hematopoietic system is well-defined. Therefore, we have to consider the risk of bone marrow suppression in NSCLC patients after receiving this treatment regimen. Our objective was to identify risk factors for the risk of myelosuppression, one of the serious complications of PD-1 inhibitor plus platinum-based dual-agent chemotherapy, in patients with NSCLC and to develop an effective machine learning (ML) model to predict this risk. Methods: We retrospectively enrolled patients with NSCLC who received PD-1 inhibitor plus platinum-doublet chemotherapy at the Department of Respiratory Medicine, The First Hospital of Lanzhou University between July 2018 and March 2026. A subset of these patients was randomly divided into a training set (70%) and a test set (30%). In the training set, feature selection was performed using recursive feature elimination (RFE), least absolute shrinkage and selection operator (LASSO), and random forest (RF). Multiple ML models were constructed and evaluated, with the area under the curve (AUC) as the primary performance metric. Model interpretability was assessed using Shapley Additive Explanations (SHAP). External validation was performed using a temporally distinct subsequent cohort. Results: Feature selection using RFE, LASSO, and RF identified age, body mass index (BMI), tumor size, platelet count, red cell distribution width (RDW), total protein, white blood cell count (WBC), and red blood cell count (RBC) as significant risk factors for myelosuppression in patients with NSCLC receiving PD-1 inhibitor plus platinum-doublet chemotherapy. Among the developed ML models, light gradient boosting machine (LightGBM) demonstrated the best performance, achieving AUCs of 0.898 in the training set, 0.841 in the test set, and 0.793 in external validation. Conclusions: The LightGBM model effectively predicts the risk of myelosuppression in patients with NSCLC receiving PD-1 inhibitor plus platinum-doublet chemotherapy and may provide useful support for clinical decision-making.