Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors - Shao, Meihua; Niu, Zhongfeng; He, Linyang; Fang, Zhaoxing; He, Jie; Xie, Zongyu; Cheng, Guohua; Wang, Jian (Frontiers in Oncology, 2021)
We aimed to build radiomics models based on triple-phase CT images combing clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). 231 patients with pathologically diagnosed GISTs from July 2012 to July 2020 were categorized into training data set (82 patients with high risk, 80 patients with low risk) and validation data set (35 patients with high risk, 34 patients with low risk) with a ratio of 7:3. Four diagnostic models were constructed by assessing 20 clinical characteristics and 18 radiomic features which were extracted from lesion mask based on triple-phase CT images. The receiver operating characteristic (ROC) curves were applied to calculate the diagnostic performance of these models and ROC curves of these models were compared using Delong test in different data sets. The results of ROC analyses showed that areas under ROC curves (AUC) of model 4 [ Clinic + CT value of unenhanced (CTU) + CT value of arterial phase (CTA) + value of venous phase (CTV)], model 1 (Clinic + CTU), model 2 (Clinic + CTA), model 3 (Clinic + CTV) were 0.925, 0.894, 0.909, and 0.914 in training set and 0.897, 0.866, 0,892, and 0.892 in validation set, respectively. The model 4, model 1, model 2, model 3 yielded an accuracy of 88.3, 85.8, 86.4, and 84.6%, a sensitivity of 85.4, 84.2, 76.8, and 78.0%, and a specificity of 91.2, 87.5, 96.2, and 91.2% in training set and an accuracy of 88.4, 84.1, 82.6, and 82.6%, a sensitivity of 88.6, 77.1, 74.3, and 85.7%, and a specificity of 88.2, 91.2, 91.2, and 79.4% in validation set, respectively. There was a significant difference between model 4 and model 1 in discriminating the risk rating in gastrointestinal stromal tumors in training data set ( Delong test, P＜0.05).The radiomic models based on clinical features and triple-phase CT images manifested excellent accuracy for the discrimination of risk rating of GISTs.