Shu-Han Xie , Wan-Fei Zhang , Yue Wu , Zi-Lu Tang , Li-Tao Yang , Yun-Jing Xue , Jiang-Bo Lin , Ming-Qiang Kang
{"title":"基于CT放射组学和机器学习的预测模型在治疗前诊断隐匿性局部晚期食管鳞状细胞癌中的应用:一项双中心研究。","authors":"Shu-Han Xie , Wan-Fei Zhang , Yue Wu , Zi-Lu Tang , Li-Tao Yang , Yun-Jing Xue , Jiang-Bo Lin , Ming-Qiang Kang","doi":"10.1016/j.tranon.2024.102050","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>Development and validation of a radiomics model for predicting occult locally advanced esophageal squamous cell carcinoma (LA-ESCC) on computed tomography (CT) radiomic features before implementation of treatment.</p></div><div><h3>Methods</h3><p>The study retrospectively collected 574 patients with esophageal squamous cell carcinoma (ESCC) from two medical centers, which were divided into three cohorts for training, internal and external validation. After delineating volume of interest (VOI), radiomics features were extracted and subjected to feature selection using three robust methods. Subsequently, 10 machine learning models were constructed, among which the optimal model was utilized to establish a radiomics signature. Furthermore, a predictive nomogram incorporating both clinical and radiomics signatures was developed. The performance of these models was evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis as well as measures including accuracy, sensitivity, and specificity.</p></div><div><h3>Results</h3><p>A total of 19 radiomics features were selected. The multilayer perceptron (MLP), which was found to be optimal, achieved an AUC of 0.919, 0.864 and 0.882 in the training, internal and external validation cohorts, respectively. Similarly, MLP showed good accuracy in distinguish occult LA-ESCC in subgroup of cT<sub>1–2</sub>N<sub>0</sub>M<sub>0</sub> diagnosed by clinicians with 0.803 and 0.789 in two validation cohorts respectively. By incorporating the radiomics signature with clinical signature, a predictive nomogram demonstrated superior prediction performance with an AUC of 0.877 and accuracy of 0.85 in external validation cohort.</p></div><div><h3>Conclusion</h3><p>The radiomics and machine learning model can offers improved accuracy in prediction of occult LA-ESCC, providing valuable assistance to clinicians when choosing treatment plans.</p></div>","PeriodicalId":48975,"journal":{"name":"Translational Oncology","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1936523324001773/pdfft?md5=0207720e49b74e9b9f1e7ceaddcf0928&pid=1-s2.0-S1936523324001773-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Application of predictive model based on CT radiomics and machine learning in diagnosis for occult locally advanced esophageal squamous cell carcinoma before treatment: A two-center study\",\"authors\":\"Shu-Han Xie , Wan-Fei Zhang , Yue Wu , Zi-Lu Tang , Li-Tao Yang , Yun-Jing Xue , Jiang-Bo Lin , Ming-Qiang Kang\",\"doi\":\"10.1016/j.tranon.2024.102050\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>Development and validation of a radiomics model for predicting occult locally advanced esophageal squamous cell carcinoma (LA-ESCC) on computed tomography (CT) radiomic features before implementation of treatment.</p></div><div><h3>Methods</h3><p>The study retrospectively collected 574 patients with esophageal squamous cell carcinoma (ESCC) from two medical centers, which were divided into three cohorts for training, internal and external validation. After delineating volume of interest (VOI), radiomics features were extracted and subjected to feature selection using three robust methods. Subsequently, 10 machine learning models were constructed, among which the optimal model was utilized to establish a radiomics signature. Furthermore, a predictive nomogram incorporating both clinical and radiomics signatures was developed. The performance of these models was evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis as well as measures including accuracy, sensitivity, and specificity.</p></div><div><h3>Results</h3><p>A total of 19 radiomics features were selected. The multilayer perceptron (MLP), which was found to be optimal, achieved an AUC of 0.919, 0.864 and 0.882 in the training, internal and external validation cohorts, respectively. Similarly, MLP showed good accuracy in distinguish occult LA-ESCC in subgroup of cT<sub>1–2</sub>N<sub>0</sub>M<sub>0</sub> diagnosed by clinicians with 0.803 and 0.789 in two validation cohorts respectively. By incorporating the radiomics signature with clinical signature, a predictive nomogram demonstrated superior prediction performance with an AUC of 0.877 and accuracy of 0.85 in external validation cohort.</p></div><div><h3>Conclusion</h3><p>The radiomics and machine learning model can offers improved accuracy in prediction of occult LA-ESCC, providing valuable assistance to clinicians when choosing treatment plans.</p></div>\",\"PeriodicalId\":48975,\"journal\":{\"name\":\"Translational Oncology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1936523324001773/pdfft?md5=0207720e49b74e9b9f1e7ceaddcf0928&pid=1-s2.0-S1936523324001773-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Translational Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1936523324001773\",\"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/S1936523324001773","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
Application of predictive model based on CT radiomics and machine learning in diagnosis for occult locally advanced esophageal squamous cell carcinoma before treatment: A two-center study
Purpose
Development and validation of a radiomics model for predicting occult locally advanced esophageal squamous cell carcinoma (LA-ESCC) on computed tomography (CT) radiomic features before implementation of treatment.
Methods
The study retrospectively collected 574 patients with esophageal squamous cell carcinoma (ESCC) from two medical centers, which were divided into three cohorts for training, internal and external validation. After delineating volume of interest (VOI), radiomics features were extracted and subjected to feature selection using three robust methods. Subsequently, 10 machine learning models were constructed, among which the optimal model was utilized to establish a radiomics signature. Furthermore, a predictive nomogram incorporating both clinical and radiomics signatures was developed. The performance of these models was evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis as well as measures including accuracy, sensitivity, and specificity.
Results
A total of 19 radiomics features were selected. The multilayer perceptron (MLP), which was found to be optimal, achieved an AUC of 0.919, 0.864 and 0.882 in the training, internal and external validation cohorts, respectively. Similarly, MLP showed good accuracy in distinguish occult LA-ESCC in subgroup of cT1–2N0M0 diagnosed by clinicians with 0.803 and 0.789 in two validation cohorts respectively. By incorporating the radiomics signature with clinical signature, a predictive nomogram demonstrated superior prediction performance with an AUC of 0.877 and accuracy of 0.85 in external validation cohort.
Conclusion
The radiomics and machine learning model can offers improved accuracy in prediction of occult LA-ESCC, providing valuable assistance to clinicians when choosing treatment plans.
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.