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

• Published in: Cancer control Vol. 32; p. 10732748251347679
• Main Authors: Wang, Haojun, Zhang, Huiru, Yao, Yan, Yu, Yang, Wang, Longyun, Liu, Ruijuan, Sun, Changgang, Zhuang, Jing
• Format: Journal Article
• Language: English
• Published: United States SAGE Publications 01.05.2025; SAGE Publishing
• Subjects: Aged; China - epidemiology; East Asian People; Female; Humans; Kaplan-Meier Estimate; Lung Neoplasms - mortality; Lung Neoplasms - pathology; Lung Neoplasms - radiotherapy; Machine Learning; Male; Middle Aged; Neoplasm Staging; Prognosis; Retrospective Studies; SEER Program - statistics & numerical data; Small Cell Lung Carcinoma - mortality; Small Cell Lung Carcinoma - pathology; Small Cell Lung Carcinoma - radiotherapy; China; radiotherapy; small cell lung cancer; machine learning; prognosis; XGBoost
• ISSN: 1073-2748; 1526-2359; 1526-2359
• DOI: 10.1177/10732748251347679

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 ( < .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.