New advances in radiomics of gastrointestinal stromal tumors

Table 1 Summary of radiomics studies including gastrointestinal stromal tumors

Ref.	Number of lesions	Imaging	Radiomics analysis	Key radiomics results
Ba-Ssalamah et al[30]	15 GISTs, 27 gastric adenocarcinomas, 5 lymphomas	CT	Histogram-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 GISTs	CT	GLCM, 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 GISTs	CT	Residual 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 GISTs	CT	Histogram-based.	AUROC of 0.782-0.779 of mpp and kurtosis for differentiation of high-risk GISTs.
Ekert et al[34]	25 GISTs	CT	Histogram-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 GISTs	CT	Histogram-based.	AUROC of 0.823-0.830 of entropy for the differentiation of low from high-risk GISTs.
Fu et al[36]	51 GISTs	MRI	Fractal features, GLCM, GLRLM.	Texture features on DWI and ADC map correlated with overall survival in metastatic GISTs.
Liu et al[37]	78 GISTs	CT	Histogram-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 PDACs	CT	Histogram-based.	AUROC of 0.809-0.936 of 90th percentile for differentiation of GISTs from DACs and PDACs.
Ren et al[39]	440 GISTs	CT	Histogram-based, GLCM.	AUROC of 0.933-0.935 for the differentiation of low from high-risk GISTs.
Wang et al[40]	333 GISTs	CT	Histogram-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 GISTs	CT	Histogram-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 GISTs	CT	Histogram-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 GISTs	CT	Histogram-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 GISTs	CT	GLCM, GLRLM, GLSZM, GLDM.	AUROC of 0.754-0.787 of radiomics features for prediction of high Ki67 expression.

AUROC: Area under the receiver operating characteristics curve; CT: Computed tomography; DACs: Duodenal adenocarcinomas; GIST: Gastrointestinal stromal tumors; GLCM: Grey level co-occurrence matrix; GLDM: Grey-level dependence matrix; GLRLM: Grey-level run length matrix; GLSZM, Gray-level size zone matrix; GLZLM: Grey-level zone length matrix; MRI: Magnetic resonance imaging; NGLDM: Neighborhood grey-level different matrix; NGTDM: Neighbourhood gray-tone difference matrix; PDACs: Pancreatic ductal adenocarcinomas.