Que N N Tran, Minh-Khang Le, Tetsuo Kondo, Takeshi Moriguchi
{"title":"基于机器学习的模型预测肺癌患者住院死亡率:一项基于人群的523,959例研究","authors":"Que N N Tran, Minh-Khang Le, Tetsuo Kondo, Takeshi Moriguchi","doi":"10.3390/arm91040025","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background:</b> Stratify new lung cancer patients based on the risk of in-hospital mortality rate after diagnosis. <b>Methods:</b> 522,941 lung cancer cases with available data on the Surveillance, Epidemiology, and End Results (SEER) were analyzed for the predicted probability based on six fundamental variables including age, gender, tumor size, T, N, and AJCC stages. The patients were randomly assigned to the training (<i>n</i> = 115,145) and validation datasets (<i>n</i> = 13,017). The remaining cohort with missing values (<i>n</i> = 394,779) was then combined with the primary lung tumour datasets (<i>n</i> = 1018) from The Cancer Genome Atlas, Lung Adenocarcinoma and Lung Squamous Cell Carcinoma projects (TCGA-LUAD & TCGA-LUSC) for external validation and sensitivity analysis. <b>Results:</b> Receiver Operating Characteristic (ROC) analyses showed high discriminatory power in the training and internal validation cohorts (Area under the curve [AUC] of 0.78 (95%CI = 0.78-0.79) and 0.78 (95%CI = 0.77-0.79), respectively), whereas that of the model on external validation data was 0.759 (95%CI = 0.757-0.761). We developed a static nomogram, a web app, and a risk table based on a logistic regression model using algorithm-selected variables. <b>Conclusions:</b> Our model can stratify lung cancer patients into high- and low-risk of in-hospital mortality to assist clinical further planning.</p>","PeriodicalId":7391,"journal":{"name":"Advances in respiratory medicine","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10451707/pdf/","citationCount":"0","resultStr":"{\"title\":\"A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 Cases.\",\"authors\":\"Que N N Tran, Minh-Khang Le, Tetsuo Kondo, Takeshi Moriguchi\",\"doi\":\"10.3390/arm91040025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Background:</b> Stratify new lung cancer patients based on the risk of in-hospital mortality rate after diagnosis. <b>Methods:</b> 522,941 lung cancer cases with available data on the Surveillance, Epidemiology, and End Results (SEER) were analyzed for the predicted probability based on six fundamental variables including age, gender, tumor size, T, N, and AJCC stages. The patients were randomly assigned to the training (<i>n</i> = 115,145) and validation datasets (<i>n</i> = 13,017). The remaining cohort with missing values (<i>n</i> = 394,779) was then combined with the primary lung tumour datasets (<i>n</i> = 1018) from The Cancer Genome Atlas, Lung Adenocarcinoma and Lung Squamous Cell Carcinoma projects (TCGA-LUAD & TCGA-LUSC) for external validation and sensitivity analysis. <b>Results:</b> Receiver Operating Characteristic (ROC) analyses showed high discriminatory power in the training and internal validation cohorts (Area under the curve [AUC] of 0.78 (95%CI = 0.78-0.79) and 0.78 (95%CI = 0.77-0.79), respectively), whereas that of the model on external validation data was 0.759 (95%CI = 0.757-0.761). We developed a static nomogram, a web app, and a risk table based on a logistic regression model using algorithm-selected variables. <b>Conclusions:</b> Our model can stratify lung cancer patients into high- and low-risk of in-hospital mortality to assist clinical further planning.</p>\",\"PeriodicalId\":7391,\"journal\":{\"name\":\"Advances in respiratory medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10451707/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in respiratory medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/arm91040025\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RESPIRATORY SYSTEM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in respiratory medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/arm91040025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 Cases.
Background: Stratify new lung cancer patients based on the risk of in-hospital mortality rate after diagnosis. Methods: 522,941 lung cancer cases with available data on the Surveillance, Epidemiology, and End Results (SEER) were analyzed for the predicted probability based on six fundamental variables including age, gender, tumor size, T, N, and AJCC stages. The patients were randomly assigned to the training (n = 115,145) and validation datasets (n = 13,017). The remaining cohort with missing values (n = 394,779) was then combined with the primary lung tumour datasets (n = 1018) from The Cancer Genome Atlas, Lung Adenocarcinoma and Lung Squamous Cell Carcinoma projects (TCGA-LUAD & TCGA-LUSC) for external validation and sensitivity analysis. Results: Receiver Operating Characteristic (ROC) analyses showed high discriminatory power in the training and internal validation cohorts (Area under the curve [AUC] of 0.78 (95%CI = 0.78-0.79) and 0.78 (95%CI = 0.77-0.79), respectively), whereas that of the model on external validation data was 0.759 (95%CI = 0.757-0.761). We developed a static nomogram, a web app, and a risk table based on a logistic regression model using algorithm-selected variables. Conclusions: Our model can stratify lung cancer patients into high- and low-risk of in-hospital mortality to assist clinical further planning.
期刊介绍:
"Advances in Respiratory Medicine" is a new international title for "Pneumonologia i Alergologia Polska", edited bimonthly and addressed to respiratory professionals. The Journal contains peer-reviewed original research papers, short communications, case-reports, recommendations of the Polish Respiratory Society concerning the diagnosis and treatment of lung diseases, editorials, postgraduate education articles, letters and book reviews in the field of pneumonology, allergology, oncology, immunology and infectious diseases. "Advances in Respiratory Medicine" is an open access, official journal of Polish Society of Lung Diseases, Polish Society of Allergology and National Research Institute of Tuberculosis and Lung Diseases.