Copyright ©The Author(s) 2020.
World J Gastroenterol. Aug 28, 2020; 26(32): 4729-4738
Published online Aug 28, 2020. doi: 10.3748/wjg.v26.i32.4729
Table 1 Summary of radiomics studies including gastrointestinal stromal tumors
Ref.Number of lesionsImagingRadiomics analysisKey radiomics results
Ba-Ssalamah et al[30]15 GISTs, 27 gastric adenocarcinomas, 5 lymphomasCTHistogram-based, GLCM, GLRLM, absolute gradient, autoregressive model, Wavelet transform.On AP texture features perfectly differentiated between GIST vs lymphoma. On PVP texture features differentiated GIST vs adenocarcinoma in 90% of cases and GIST vs lymphoma in 92% of cases.
Chen et al[31]222 GISTsCTGLCM, GLRLM, GLSZM, NGTDM. Support Vector Machine for model building.AUROC 0.84-0.86 of radiomics models for GIST risk stratification.
Chen et al[32]147 GISTsCTResidual Neural Network for model building.AUROC of 0.887-0.947 for ResNet nomogram and model for prediction of disease-free survival after surgical resection.
Choi et al[33]145 GISTsCTHistogram-based.AUROC of 0.782-0.779 of mpp and kurtosis for differentiation of high-risk GISTs.
Ekert et al[34]25 GISTsCTHistogram-based, GLCM, GLDM, GLRLM, GLSZM, NGLDM.Ten GLCM, GLRLM, NGLDM features significantly correlated with disease progression and progression free survival.
Feng et al[35]90 GISTsCTHistogram-based.AUROC of 0.823-0.830 of entropy for the differentiation of low from high-risk GISTs.
Fu et al[36]51 GISTsMRIFractal features, GLCM, GLRLM.Texture features on DWI and ADC map correlated with overall survival in metastatic GISTs.
Liu et al[37]78 GISTsCTHistogram-based.AUROC of 0.637-0.811 for the identification of very low and low-risk GISTs.
Lu et al[38]28 GISTs, 26 DACs, 20 PDACsCTHistogram-based.AUROC of 0.809-0.936 of 90th percentile for differentiation of GISTs from DACs and PDACs.
Ren et al[39]440 GISTsCTHistogram-based, GLCM.AUROC of 0.933-0.935 for the differentiation of low from high-risk GISTs.
Wang et al[40]333 GISTsCTHistogram-based, GLCM, GLRLM.AUROC of 0.882-0.920 for the differentiation of low from high-risk GISTs. AUROC of 0.769-0.820 for the differentiation of low from high mitotic count.
Xu et al[41]86 GISTsCTHistogram-based, GLCM, GLRLM.AUROC of 0.904-0.962 of standard deviation for diagnosis of GIST without KIT exon 11 mutations.
Yan et al[42]213 GISTsCTHistogram-based, GLCM, GLRLM, absolute gradient, autoregressive model, Wavelet transform.AUROC of 0.933 of texture analysis model for preoperative risk stratification.
Zhang et al[43]140 GISTsCTHistogram-based, shape-based, GLCM, GLRLM, GLSZM.AUROC of 0.809-0.935 for discrimination of advanced GISTs and four risk categories of GISTs
Zhang et al[44]339 GISTsCTGLCM, GLRLM, GLSZM, GLDM.AUROC of 0.754-0.787 of radiomics features for prediction of high Ki67 expression.