Discriminating between T2 and T3 staging in patients with esophageal cancer using deep learning and radiomic features based on arterial phase CT imaging
10.12354/j.issn.1000-8179.2024.20240859
- VernacularTitle:基于食管癌动脉期CT图像的深度学习和影像组学特征预测其T2 T3分期
- Author:
Liu XUECHENG
1
;
Wu SHUJIAN
;
Yao QI
;
Feng LEI
;
Wang JUAN
;
Zhou YUNFENG
Author Information
1. 皖南医学院第一附属医院放射科(安徽省芜湖市 241000)
- Keywords:
esophageal cancer;
deep learning;
radiomics;
CT imaging;
TNM staging
- From:
Chinese Journal of Clinical Oncology
2024;51(14):728-736
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the application of combined deep learning and radiomic features derived from enhanced arterial phase CT imaging with clinical data to differentiate between T2 and T3 staging in patients with esophageal cancer.Methods:A retrospective study was conducted using clinical and CT data from 388 patients with pathologically confirmed esophageal cancer treated at The First Affiliated Hospital of Wannan Medical College between May 2015 and April 2024.The dataset was randomly divided into a training set(271 cases)and validation set(117 cases)in a 7:3 ratio.Radiomic and deep learning features were extracted from enhanced arterial phase CT images.The least absolute shrinkage and selection operator algorithm was employed for feature reduction and selection,leading to the development of radiomic(Radscore)and deep learning(Deepscore)scores.Univariate and multivariate Logistic regression analyses were conducted to identify independent risk factors,and clinical,radiomic,deep learning,and combined models were constructed.A nomogram was gener-ated for the combined model.The diagnostic performance of the models was evaluated using the area under the receiver operating charac-teristic curve(AUC)and compared using the DeLong test.Clinical net benefit was assessed through decision curve analysis,and model calib-ration was evaluated using calibration curves.Results:Nine radiomicand 12 deep learning features were selected after dimensionality reduc-tion.Multivariate Logistic regression identified tumor length,boundary,Radscore,and Deepscore as independent risk factors for distinguish-ing between T2 and T3 staging.In the training set,the AUC of the combined model was 0.867,which was significantly higher than that of the clinical(0.774,P<0.001),radiomic(0.795,P<0.001),and deep learning(0.821,P=0.001)models.In the validation set,the AUC of the com-bined model was 0.810,which was significantly higher than that of the clinical(0.653,P=0.002),radiomic(0.719,P=0.033),and deep learn-ing(0.750,P=0.009)models.The decision curve analysis indicated that the combined model provided the highest clinical benefit in both datasets.The calibration curves demonstrated a good fit for both datasets(P=0.084,0.053).Conclusion:The integration of deep learning and radiomic features obtained from enhanced arterial phase CT images with clinical data offers a reliable method for accurately distinguishing between preoperative T2 and T3 staging in esophageal cancer,thereby supporting clinical decision-making for treatment planning.
