{"title":"通过放射组学和基因组学特征,建立早期非小细胞肺癌气隙扩散(STAS)预测模型。","authors":"Yimin Wang, Chuling Li, Zhaofeng Wang, Ranpu Wu, Huijuan Li, Yunchang Meng, Hongbing Liu, Yong Song","doi":"10.1111/ajco.14099","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We retrospectively collected 204 patients (47 STAS+ and 157 STAS−) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC] = 0.85; AUC = 0.70; AUC = 0.85).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The prediction model based on radiomics and genomics features has a good prediction performance for STAS.</p>\n </section>\n </div>","PeriodicalId":8633,"journal":{"name":"Asia-Pacific journal of clinical oncology","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Established the prediction model of early-stage non-small cell lung cancer spread through air spaces (STAS) by radiomics and genomics features\",\"authors\":\"Yimin Wang, Chuling Li, Zhaofeng Wang, Ranpu Wu, Huijuan Li, Yunchang Meng, Hongbing Liu, Yong Song\",\"doi\":\"10.1111/ajco.14099\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We retrospectively collected 204 patients (47 STAS+ and 157 STAS−) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC] = 0.85; AUC = 0.70; AUC = 0.85).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>The prediction model based on radiomics and genomics features has a good prediction performance for STAS.</p>\\n </section>\\n </div>\",\"PeriodicalId\":8633,\"journal\":{\"name\":\"Asia-Pacific journal of clinical oncology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asia-Pacific journal of clinical oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/ajco.14099\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asia-Pacific journal of clinical oncology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ajco.14099","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
Established the prediction model of early-stage non-small cell lung cancer spread through air spaces (STAS) by radiomics and genomics features
Background
This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features.
Methods
We retrospectively collected 204 patients (47 STAS+ and 157 STAS−) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3).
Results
The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC] = 0.85; AUC = 0.70; AUC = 0.85).
Conclusions
The prediction model based on radiomics and genomics features has a good prediction performance for STAS.
期刊介绍:
Asia–Pacific Journal of Clinical Oncology is a multidisciplinary journal of oncology that aims to be a forum for facilitating collaboration and exchanging information on what is happening in different countries of the Asia–Pacific region in relation to cancer treatment and care. The Journal is ideally positioned to receive publications that deal with diversity in cancer behavior, management and outcome related to ethnic, cultural, economic and other differences between populations. In addition to original articles, the Journal publishes reviews, editorials, letters to the Editor and short communications. Case reports are generally not considered for publication, only exceptional papers in which Editors find extraordinary oncological value may be considered for review. The Journal encourages clinical studies, particularly prospectively designed clinical trials.