Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy
IntroductionSmall cell lung cancer (SCLC) is a highly aggressive form of lung cancer, and chemotherapy remains a cornerstone of its management. However, the treatment is associated with significant risks, including heightened toxicity and early mortality. This study aimed to quantify the 90-day mort...
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Frontiers Media S.A.
2025-01-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1483097/full |
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| author | Min Liang Min Liang Fuyuan Luo |
| author_facet | Min Liang Min Liang Fuyuan Luo |
| author_sort | Min Liang |
| collection | DOAJ |
| description | IntroductionSmall cell lung cancer (SCLC) is a highly aggressive form of lung cancer, and chemotherapy remains a cornerstone of its management. However, the treatment is associated with significant risks, including heightened toxicity and early mortality. This study aimed to quantify the 90-day mortality rate post-chemotherapy in SCLC patients, identify associated features, and develop a predictive machine learning model.MethodsThis study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database (2000–2018) to identify prognostic features influencing early mortality in SCLC patients. Prognostic features were selected through univariate logistic regression and Lasso analyses. Predictive modeling was performed using advanced machine learning algorithms, including XGBoost, Multilayer Perceptron, K-Nearest Neighbor, and Random Forest. Additionally, traditional models, such as logistic regression and AJCC staging, were employed for comparison. Model performance was evaluated using key metrics, including the Area Under the Receiver Operating Characteristic Curve (AUC), calibration plots, the Kolmogorov–Smirnov (KS) statistic, and Decision Curve Analysis (DCA).ResultsAnalysis of 12,500 eligible patients revealed 10 clinical features significantly impacting outcomes. The XGBoost model demonstrated superior discriminatory capability, achieving AUC scores of 0.95 in the training set and 0.78 in the validation set. It outperformed comparative models across all datasets, as evidenced by its AUC, KS score, calibration, and DCA results. Additionally, the model was integrated into a web-based platform to improve accessibility.ConclusionThis study introduces a machine learning model alongside a web-based support system as critical resources for healthcare professionals, facilitating personalized clinical decision-making and enhancing treatment strategies for SCLC patients post-chemotherapy. |
| format | Article |
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| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
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| series | Frontiers in Medicine |
| spelling | doaj-art-5f93b9bf29aa47b6b3b7371d5c4d1f202025-01-24T07:13:45ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011210.3389/fmed.2025.14830971483097Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapyMin Liang0Min Liang1Fuyuan Luo2Department of Respiratory and Critical Care Medicine, Maoming People’s Hospital, Maoming, ChinaCenter of Respiratory Research, Maoming People’s Hospital, Maoming, ChinaDepartment of Respiratory and Critical Care Medicine, Gaozhou People's Hospital, Maoming, ChinaIntroductionSmall cell lung cancer (SCLC) is a highly aggressive form of lung cancer, and chemotherapy remains a cornerstone of its management. However, the treatment is associated with significant risks, including heightened toxicity and early mortality. This study aimed to quantify the 90-day mortality rate post-chemotherapy in SCLC patients, identify associated features, and develop a predictive machine learning model.MethodsThis study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database (2000–2018) to identify prognostic features influencing early mortality in SCLC patients. Prognostic features were selected through univariate logistic regression and Lasso analyses. Predictive modeling was performed using advanced machine learning algorithms, including XGBoost, Multilayer Perceptron, K-Nearest Neighbor, and Random Forest. Additionally, traditional models, such as logistic regression and AJCC staging, were employed for comparison. Model performance was evaluated using key metrics, including the Area Under the Receiver Operating Characteristic Curve (AUC), calibration plots, the Kolmogorov–Smirnov (KS) statistic, and Decision Curve Analysis (DCA).ResultsAnalysis of 12,500 eligible patients revealed 10 clinical features significantly impacting outcomes. The XGBoost model demonstrated superior discriminatory capability, achieving AUC scores of 0.95 in the training set and 0.78 in the validation set. It outperformed comparative models across all datasets, as evidenced by its AUC, KS score, calibration, and DCA results. Additionally, the model was integrated into a web-based platform to improve accessibility.ConclusionThis study introduces a machine learning model alongside a web-based support system as critical resources for healthcare professionals, facilitating personalized clinical decision-making and enhancing treatment strategies for SCLC patients post-chemotherapy.https://www.frontiersin.org/articles/10.3389/fmed.2025.1483097/fullsmall cell lung cancerearly mortalitymachine learningsurvivalchemotherapy |
| spellingShingle | Min Liang Min Liang Fuyuan Luo Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy Frontiers in Medicine small cell lung cancer early mortality machine learning survival chemotherapy |
| title | Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy |
| title_full | Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy |
| title_fullStr | Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy |
| title_full_unstemmed | Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy |
| title_short | Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy |
| title_sort | machine learning insights into early mortality risks for small cell lung cancer patients post chemotherapy |
| topic | small cell lung cancer early mortality machine learning survival chemotherapy |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1483097/full |
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