{"title":"利用对比增强 CT 成像开发和验证非小细胞肺癌 TACC3 表达和预后的放射学预测模型","authors":"Weichao Bai , Xinhan Zhao , Qian Ning","doi":"10.1016/j.tranon.2024.102211","DOIUrl":null,"url":null,"abstract":"<div><h3>Backgrounds</h3><div>Non-small cell lung cancer (NSCLC) prognosis remains poor despite treatment advances, and classical prognostic indicators often fall short in precision medicine. Transforming acidic coiled-coil protein-3 (<em>TACC3</em>) has been identified as a critical factor in tumor progression and immune infiltration across cancers, including NSCLC. Predicting <em>TACC3</em> expression through radiomic features may provide valuable insights into tumor biology and aid clinical decision-making. However, its predictive value in NSCLC remains unexplored. Therefore, we aimed to construct and validate a radiomic model to predict <em>TACC3</em> levels and prognosis in patients with NSCLC.</div></div><div><h3>Materials and methods</h3><div>Genomic data and contrast-enhanced computed tomography (CT) images were sourced from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) database, and The Cancer Imaging Archive (TCIA). A total of 320 cases of lung adenocarcinoma from TCGA and 122 cases of NSCLC from GEO were used for prognostic analysis. Sixty-three cases from TCIA and GEO were included for radiomics feature extraction and model development. The radiomics model was constructed using logistic regression (LR) and support vector machine (SVM) algorithms. We predicted <em>TACC3</em> expression and evaluated its correlation with NSCLC prognosis using contrast-enhanced CT-based radiomics.</div></div><div><h3>Results</h3><div><em>TACC3</em> expression significantly influenced NSCLC prognosis. High TACC3 levels were associated with reduced overall survival, potentially mediated by immune microenvironment and tumor progression regulation. LR and SVM algorithms achieved AUC of 0.719 and 0.724, respectively, which remained at 0.701 and 0.717 after five-fold cross-validation.</div></div><div><h3>Conclusion</h3><div>Contrast-enhanced CT-based radiomics can non-invasively predict <em>TACC3</em> expression and provide valuable prognostic information, contributing to personalized treatment strategies.</div></div>","PeriodicalId":48975,"journal":{"name":"Translational Oncology","volume":"51 ","pages":"Article 102211"},"PeriodicalIF":5.0000,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a radiomic prediction model for TACC3 expression and prognosis in non-small cell lung cancer using contrast-enhanced CT imaging\",\"authors\":\"Weichao Bai , Xinhan Zhao , Qian Ning\",\"doi\":\"10.1016/j.tranon.2024.102211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Backgrounds</h3><div>Non-small cell lung cancer (NSCLC) prognosis remains poor despite treatment advances, and classical prognostic indicators often fall short in precision medicine. Transforming acidic coiled-coil protein-3 (<em>TACC3</em>) has been identified as a critical factor in tumor progression and immune infiltration across cancers, including NSCLC. Predicting <em>TACC3</em> expression through radiomic features may provide valuable insights into tumor biology and aid clinical decision-making. However, its predictive value in NSCLC remains unexplored. Therefore, we aimed to construct and validate a radiomic model to predict <em>TACC3</em> levels and prognosis in patients with NSCLC.</div></div><div><h3>Materials and methods</h3><div>Genomic data and contrast-enhanced computed tomography (CT) images were sourced from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) database, and The Cancer Imaging Archive (TCIA). A total of 320 cases of lung adenocarcinoma from TCGA and 122 cases of NSCLC from GEO were used for prognostic analysis. Sixty-three cases from TCIA and GEO were included for radiomics feature extraction and model development. The radiomics model was constructed using logistic regression (LR) and support vector machine (SVM) algorithms. We predicted <em>TACC3</em> expression and evaluated its correlation with NSCLC prognosis using contrast-enhanced CT-based radiomics.</div></div><div><h3>Results</h3><div><em>TACC3</em> expression significantly influenced NSCLC prognosis. High TACC3 levels were associated with reduced overall survival, potentially mediated by immune microenvironment and tumor progression regulation. LR and SVM algorithms achieved AUC of 0.719 and 0.724, respectively, which remained at 0.701 and 0.717 after five-fold cross-validation.</div></div><div><h3>Conclusion</h3><div>Contrast-enhanced CT-based radiomics can non-invasively predict <em>TACC3</em> expression and provide valuable prognostic information, contributing to personalized treatment strategies.</div></div>\",\"PeriodicalId\":48975,\"journal\":{\"name\":\"Translational Oncology\",\"volume\":\"51 \",\"pages\":\"Article 102211\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-11-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Translational Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1936523324003383\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational Oncology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1936523324003383","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
Development and validation of a radiomic prediction model for TACC3 expression and prognosis in non-small cell lung cancer using contrast-enhanced CT imaging
Backgrounds
Non-small cell lung cancer (NSCLC) prognosis remains poor despite treatment advances, and classical prognostic indicators often fall short in precision medicine. Transforming acidic coiled-coil protein-3 (TACC3) has been identified as a critical factor in tumor progression and immune infiltration across cancers, including NSCLC. Predicting TACC3 expression through radiomic features may provide valuable insights into tumor biology and aid clinical decision-making. However, its predictive value in NSCLC remains unexplored. Therefore, we aimed to construct and validate a radiomic model to predict TACC3 levels and prognosis in patients with NSCLC.
Materials and methods
Genomic data and contrast-enhanced computed tomography (CT) images were sourced from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) database, and The Cancer Imaging Archive (TCIA). A total of 320 cases of lung adenocarcinoma from TCGA and 122 cases of NSCLC from GEO were used for prognostic analysis. Sixty-three cases from TCIA and GEO were included for radiomics feature extraction and model development. The radiomics model was constructed using logistic regression (LR) and support vector machine (SVM) algorithms. We predicted TACC3 expression and evaluated its correlation with NSCLC prognosis using contrast-enhanced CT-based radiomics.
Results
TACC3 expression significantly influenced NSCLC prognosis. High TACC3 levels were associated with reduced overall survival, potentially mediated by immune microenvironment and tumor progression regulation. LR and SVM algorithms achieved AUC of 0.719 and 0.724, respectively, which remained at 0.701 and 0.717 after five-fold cross-validation.
Conclusion
Contrast-enhanced CT-based radiomics can non-invasively predict TACC3 expression and provide valuable prognostic information, contributing to personalized treatment strategies.
期刊介绍:
Translational Oncology publishes the results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of oncology patients. Translational Oncology will publish laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer. Peer reviewed manuscript types include Original Reports, Reviews and Editorials.