Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors

Abstract

We aimed to build radiomics models based on triple-phase CT images combining clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). A total of 231 patients with pathologically diagnosed GISTs from July 2012 to July 2020 were categorized into a training data set (82 patients with high risk, 80 patients with low risk) and a 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 that were extracted from a 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), and model 3 (Clinic + CTV) were 0.925, 0.894, 0.909, and 0.914 in the training set and 0.897, 0.866, 0,892, and 0.892 in the validation set, respectively. Model 4, model 1, model 2, and 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 the 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 the validation set, respectively. There was a significant difference between model 4 and model 1 in discriminating the risk rating in gastrointestinal stromal tumors in the 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.

Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors

“…In ultrasonography, cystic degeneration and necrosis appear as an intratumoral inhomogeneous echogenicity. Previous studies (27)(28)(29)(30) have shown that the presence of cystic degeneration and necrosis in the mass can be used as reliable indicators for GISTs malignancy evaluation, which is consistent with the results of our study. In this study, the AUC of the clinical ultrasound nomogram for predicting the malignant potential of GISTs was 0.86 in the training cohort and only 0.83 in the validation cohort.…”

Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor

“…Several prior studies have reported the satisfactory performance of CT-based radiomics in the diagnosis and prediction of the malignant potential of GISTs[ 17 , 37 - 39 ]. Starmans et al [ 40 ] documented that the radiomics model achieved an AUC of 0.77 in distinguishing GISTs from non-GISTs, yielding results comparable to those of radiologists but with reduced observer dependence.…”