Which Types of Patients With Extensive-Stage Small Cell Lung Cancer Benefit From Radiotherapy? A Retrospective Study Integrating Machine Learning With the SEER Database and a Chinese Cohort

IntroductionAccurate machine learning-based prognostic models for the diagnosis and treatment of extensive-stage small cell lung cancer (ES-SCLC) are currently lacking, and the role of radiotherapy in ES-SCLC remains a subject of ongoing debate.MethodsThis study used data from the Surveillance, Epidemiology, and End Results (SEER) database of patients diagnosed with ES-SCLC. Cox regression analysis was performed to identify the key prognostic factors. Six machine learning models were developed: XGBoost, support vector machine, k-nearest neighbors, random forest, Iterative Dichotomiser 3, and logistic regression. External validation was conducted using the medical records of ES-SCLC patients who met the screening criteria at a local hospital. Propensity score matching was applied to address baseline imbalance. Kaplan-Meier (K-M) survival analysis was used to evaluate the prognostic impact of radiotherapy, followed by stratified K-M analysis to further explore its applicability across subgroups.ResultsThe analysis revealed that radiotherapy, chemotherapy, and liver metastasis were significantly associated with prognosis (<i>P</i> < .001). Liver metastasis was an independent risk factor of poor survival. The stratified K-M analysis suggested that radiotherapy may benefit certain patient subgroups.ConclusionThis study provides novel insights into radiotherapy indications for ES-SCLC, contributing to improved clinical guidelines and treatment strategies based on machine learning-derived prognostic models.