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
Introduction Accurate 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. Methods This study used data from the Surveillance, E...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-05-01
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| Series: | Cancer Control |
| Online Access: | https://doi.org/10.1177/10732748251347679 |
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| author | Haojun Wang MM Huiru Zhang MM Yan Yao MD Yang Yu MD Longyun Wang MD Ruijuan Liu MD Changgang Sun PhD Jing Zhuang PhD |
| author_facet | Haojun Wang MM Huiru Zhang MM Yan Yao MD Yang Yu MD Longyun Wang MD Ruijuan Liu MD Changgang Sun PhD Jing Zhuang PhD |
| author_sort | Haojun Wang MM |
| collection | DOAJ |
| description | Introduction Accurate 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. Methods This 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. Results The analysis revealed that radiotherapy, chemotherapy, and liver metastasis were significantly associated with prognosis ( P < .001). Liver metastasis was an independent risk factor of poor survival. The stratified K-M analysis suggested that radiotherapy may benefit certain patient subgroups. Conclusion This 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. |
| format | Article |
| id | doaj-art-2566ff53b33942d4a1264e02ed7eddc8 |
| institution | Kabale University |
| issn | 1526-2359 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Control |
| spelling | doaj-art-2566ff53b33942d4a1264e02ed7eddc82025-08-20T03:37:22ZengSAGE PublishingCancer Control1526-23592025-05-013210.1177/10732748251347679Which 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 CohortHaojun Wang MMHuiru Zhang MMYan Yao MDYang Yu MDLongyun Wang MDRuijuan Liu MDChanggang Sun PhDJing Zhuang PhDIntroduction Accurate 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. Methods This 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. Results The analysis revealed that radiotherapy, chemotherapy, and liver metastasis were significantly associated with prognosis ( P < .001). Liver metastasis was an independent risk factor of poor survival. The stratified K-M analysis suggested that radiotherapy may benefit certain patient subgroups. Conclusion This 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.https://doi.org/10.1177/10732748251347679 |
| spellingShingle | Haojun Wang MM Huiru Zhang MM Yan Yao MD Yang Yu MD Longyun Wang MD Ruijuan Liu MD Changgang Sun PhD Jing Zhuang PhD 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 Cancer Control |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://doi.org/10.1177/10732748251347679 |
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