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

IF 1.8 Q3 RESPIRATORY SYSTEM
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,&nbsp;Minh-Khang Le,&nbsp;Tetsuo Kondo,&nbsp;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":"91 4","pages":"310-323"},"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,&nbsp;Minh-Khang Le,&nbsp;Tetsuo Kondo,&nbsp;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\":\"91 4\",\"pages\":\"310-323\"},\"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}
引用次数: 0

摘要

背景:基于诊断后住院死亡率的风险对新发肺癌患者进行分层。方法:根据年龄、性别、肿瘤大小、T、N和AJCC分期等6个基本变量,对522941例肺癌患者的监测、流行病学和最终结果(SEER)数据进行预测概率分析。患者被随机分配到训练数据集(n = 115145)和验证数据集(n = 13017)。然后将缺失值的剩余队列(n = 394,779)与来自癌症基因组图谱、肺腺癌和肺鳞状细胞癌项目(TCGA-LUAD和TCGA-LUSC)的原发性肺肿瘤数据集(n = 1018)相结合,进行外部验证和敏感性分析。结果:受试者工作特征(Receiver Operating Characteristic, ROC)分析显示,训练组和内部验证组的鉴别力较高(曲线下面积(Area under the curve, AUC)分别为0.78 (95%CI = 0.78-0.79)和0.78 (95%CI = 0.77-0.79),而外部验证组的鉴别力为0.759 (95%CI = 0.757-0.761)。我们开发了一个静态nomogram,一个web app,以及一个基于逻辑回归模型的风险表,该模型使用算法选择变量。结论:我们的模型可以将肺癌患者分为院内死亡率的高风险和低风险,以帮助临床进一步规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 Cases.

A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 Cases.

A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 Cases.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Advances in respiratory medicine
Advances in respiratory medicine RESPIRATORY SYSTEM-
CiteScore
2.60
自引率
0.00%
发文量
90
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信