Abstract
BackgroundWe conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs).MethodsForty-one patients (low-risk = 17, intermediate-risk = 13, high-risk = 11) underwent MRI before surgery between September 2013 and March 2019 in this retrospective study. The Kruskal–Wallis test with Bonferonni correction and variance threshold was used to select appropriate features, and the Random Forest model (three classification model) was used to select features among the high-risk, intermediate-risk, and low-risk of GISTs. The predictive performance of the models built by the Random Forest was estimated by a 5-fold cross validation (5FCV). Their performance was estimated using the receiver operating characteristic (ROC) curve, summarized as the area under the ROC curve (AUC). Area under the curve (AUC), accuracy, sensitivity, and specificity for risk classification were reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics models.ResultsThe high-risk, intermediate-risk, and low-risk of GISTs were well classified by radiomics models, the micro-average of ROC curves was 0.85, 0.81, 0.87 and 0.94 for T1WI, T2WI, ADC and combined three MR sequences. And ROC curves achieved excellent AUCs for T1WI (0.85, 0.75 and 0.82), T2WI (0.69, 0.78 and 0.78), ADC (0.85, 0.77 and 0.80) and combined three MR sequences (0.96, 0.92, 0.81) for the diagnosis of high-risk, intermediate-risk, and low-risk of GISTs, respectively. In addition, LDA demonstrated the different risk of GISTs were correctly classified by radiomics analysis (61.0% for T1WI, 70.7% for T2WI, 83.3% for ADC, and 78.9% for the combined three MR sequences).ConclusionsRadiomics models based on a single sequence and combined three MR sequences can be a noninvasive method to evaluate the risk classification of GISTs, which may help the treatment of GISTs patients in the future.
Highlights
Gastrointestinal stromal tumors (GISTs) is a rare sarcoma of soft tissue that can occur anywhere in the gastrointestinal tract, affecting 6–20 people per million per year in Western and Asian countries [1, 2]
Radiomics features such as Grey Level Nonuniformity, Run Length Nonuniformity, Volume were significantly different among the three GISTs risks groups on three sequences
0.94 limited accuracy [20,21,22].We found that some radiomics features were significantly different among the three risk classifications of GISTs
Summary
Gastrointestinal stromal tumors (GISTs) is a rare sarcoma of soft tissue that can occur anywhere in the gastrointestinal tract, affecting 6–20 people per million per year in Western and Asian countries [1, 2]. The recognized standard for risk classification of GISTs is the National Institutes of Health (revised in 2008), which can be classified as high-risk, intermediate-risk, low-risk and very lowrisk, according to tumor size, mitotic index, and primary tumor site [6]. For high-risk GISTs patients, previous studies have shown that preoperative targeted drug therapy, such as Imatinib, can shrink the tumor and limit the scope of surgical resection, and improve the prognosis of patients with GISTs [8, 9]. Accurate preoperative assessment the risk of GISTs has high clinical value, which can provide important clues for predicting the prognosis of the disease and the use of adjuvant chemotherapy. We conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs)
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