[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120264-en":3,"doc-seo-120264-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120264,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy - original research","A study quantifies 90-day mortality after chemotherapy in small cell lung cancer patients, aiming to characterize prognostic features and build a predictive machine learning model. Data were drawn from the SEER database (2000–2018), with feature selection via univariate logistic regression and Lasso. Multiple machine learning algorithms, including XGBoost, were assessed against traditional approaches. The XGBoost model showed strong discrimination and calibration, and was provided through a web-based support platform for personalized clinical decision-making.","OPEN ACCESS  \nEDITED BY  \nWenlin Yang,  \nUniversity of Florida, United States  \nREVIEWED BY  \nBeatriz Pontes, Sevilla University, Spain Yiting Wang,  \nFlorida International University, United States  \n*CORRESPONDENCE  \nFuyuan Luo  \n [gzsrmyylfy@163.com](gzsrmyylfy@163.com)  \nRECEIVED 19 August 2024  \nACCEPTED 13 January 2025  \nPUBLISHED 24 January 2025  \nCITATION  \nLiang M and Luo F (2025) Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy. Front. Med. 12:1483097.  \ndoi: 10.3389/fmed.2025.1483097  \nCOPYRIGHT  \n© 2025 Liang and Luo. This is an  \nopen-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) and the copyright owner(s) 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.  \nTYPE Original Research PUBLISHED 24 January 2025  \nDOI 10.3389/fmed.2025.1483097  \nMachine learning insights into early mortality risks for small cell lung cancer patients  \npost-chemotherapy  \nMin Liang1,2 and Fuyuan Luo3*  \n1 Department of Respiratory and Critical Care Medicine, Maoming People’s Hospital, Maoming, China, 2Center of Respiratory Research, Maoming People’s Hospital, Maoming, China, 3 Department of Respiratory and Critical Care Medicine, Gaozhou People's Hospital, Maoming, China  \nIntroduction: 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 postchemotherapy in SCLC patients, identify associated features, and develop a predictive machine learning model.  \nMethods: This 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, includingthe Area Under the Receiver Operating Characteristic Curve (AUC), calibration plots, the Kolmogorov–Smirnov (KS) statistic, and Decision Curve Analysis (DCA) .  \nResults: Analysis 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.  \nConclusion: 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.  \nKEYWORDS  \nsmall cell lung cancer, early mortality, machine learning, survival, chemotherapy  \nIntroduction  \nLung cancer ranks as the foremost cancer type worldwide and remains the principal cause of death ( 1). Small cell lung cancer (SCLC), representing about 10–15% of all lung cancer pathologies, is notorious for its aggressive nature, low degree of differentiation, rapid advancement, and bleak outcomes (2). Research reveals a stark prognosis for patients with  \nFrontiers in Medicine 01 [fro","cbCaib1qTq1l9pOO","https://ap.wps.com/l/cbCaib1qTq1l9pOO","pdf",2335924,1,13,"English","en",105,"# Introduction\n## Small cell lung cancer and chemotherapy challenges\n## Rationale and research gap\n# Methods\n## Data source and cohort definition\n## Feature selection\n## Predictive modeling and comparison\n## Evaluation metrics\n# Results\n## Patient cohort and key prognostic features\n## Model performance and validation\n## Web-based platform\n# Conclusion\n## Clinical decision support and impact","[{\"question\":\"What was the study’s primary goal?\",\"answer\":\"To quantify 90-day mortality after chemotherapy in small cell lung cancer patients, identify associated prognostic features, and develop a predictive machine learning model.\"},{\"question\":\"Which dataset and time range were used?\",\"answer\":\"The study used the Surveillance, Epidemiology, and End Results (SEER) database covering years 2000–2018.\"},{\"question\":\"Which model performed best and how was it evaluated?\",\"answer\":\"The XGBoost model showed the best performance, assessed using AUC, calibration plots, KS statistic, and Decision Curve Analysis (DCA), with AUC reported for training and validation sets.\"}]","Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy - 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