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ORIGINAL RESEARCH article

Front. Med.
Sec. Pulmonary Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1483097
This article is part of the Research Topic Clinical prediction models in cancer through bioinformatics View all 3 articles

Machine Learning Insights into Early Mortality Risks for Small Cell Lung Cancer Patients Post-Chemotherapy

Provisionally accepted
Min Liang Min Liang 1Fuyuan Luo Fuyuan Luo 2*
  • 1 Maoming People's Hospital, Maoming, China
  • 2 Department of Respiratory and Critical Care Medicine, Gaozhou People's Hospital, Maoming, China

The final, formatted version of the article will be published soon.

    Introduction: Small 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.This study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) to identify prognostic factors 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).: Analysis of 12,500 eligible patients revealed ten 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. Conclusions: This 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.

    Keywords: Small Cell Lung Cancer, Early mortality, machine learning, Survival, chemotherapy

    Received: 19 Aug 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Liang and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Fuyuan Luo, Department of Respiratory and Critical Care Medicine, Gaozhou People's Hospital, Maoming, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.