Jia Fu1, Meng-Jie Fang2, Di Dong2, Jian Li3, Ying-Shi Sun4, Jie Tian5, Lei Tang6. 1. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, 100142, China; Department of Radiology, Civil Aviation General Hospital, No. 1 Chaoyang Road, Chaoyang District, Beijing, 100123, China. 2. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 East Zhongguancun Road, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China. 3. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Departments of Gastroenterology, Peking University Cancer Hospital & Institute, Beijing, 100142, China. 4. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, 100142, China. 5. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 East Zhongguancun Road, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China. Electronic address: jie.tian@ia.ac.cn. 6. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, 100142, China. Electronic address: tangl@bjcancer.org.
Abstract
PURPOSE: To determine if texture features of diffusion weighted imaging (DWI) on MRI of metastatic gastrointestinal stromal tumor (mGIST) have correlation with overall survival (OS). METHOD: Fifty-one GIST patients with metastatic lesions who received imatinib targeted therapy were included. Texture features of the largest metastatic lesion were analyzed using inhouse software. Three types of texture features were assessed: fractal features, gray-level co-occurrence matrix (GLCM) features, and gray-level run-length matrix (GLRLM) features. The features were extracted from the regions of interest (ROIs) on T2-weighted imaging (T2WI), DWI and apparent diffusion coefficient (ADC) maps. Histogram analysis was performed on ADC maps. Patients were followed up until death. Kaplan-Meier analysis was performed to determine the correlation of texture features with OS. The curves of the high- and low-risk groups were compared using log-rank test. The prognostic efficacy of the predictors was assessed by calculating the concordance probability. RESULTS: The median survival time was 43.5 months (range, 3.97-120.90 m). Four DWI and three ADC texture features showed significant correlation with OS on univariate analysis (p < 0.05). DWI_L_GLCM_maximum_probability [hazard ratio (HR): 2.062 (1.357-3.131)], ADC_H_GLRLM_mean [HR: 2.174 (1.457-3.244)], and ADC_O_GLCM_cluster_shade [HR: 1.882 (1.324-2.674)] were identified as representative prognostic indicators. The optimum threshold levels for these three features were 1.19×100, 1.71×10 and 2.19×0.1, respectively. Neither histogram analysis values nor fractal features revealed significant correlation with survival status (p > 0.05). CONCLUSIONS: Texture features of the mGIST on DWI exhibited correlation with overall survival. High-grade heterogeneity was associated with poor prognosis.
PURPOSE: To determine if texture features of diffusion weighted imaging (DWI) on MRI of metastatic gastrointestinal stromal tumor (mGIST) have correlation with overall survival (OS). METHOD: Fifty-one GISTpatients with metastatic lesions who received imatinib targeted therapy were included. Texture features of the largest metastatic lesion were analyzed using inhouse software. Three types of texture features were assessed: fractal features, gray-level co-occurrence matrix (GLCM) features, and gray-level run-length matrix (GLRLM) features. The features were extracted from the regions of interest (ROIs) on T2-weighted imaging (T2WI), DWI and apparent diffusion coefficient (ADC) maps. Histogram analysis was performed on ADC maps. Patients were followed up until death. Kaplan-Meier analysis was performed to determine the correlation of texture features with OS. The curves of the high- and low-risk groups were compared using log-rank test. The prognostic efficacy of the predictors was assessed by calculating the concordance probability. RESULTS: The median survival time was 43.5 months (range, 3.97-120.90 m). Four DWI and three ADC texture features showed significant correlation with OS on univariate analysis (p < 0.05). DWI_L_GLCM_maximum_probability [hazard ratio (HR): 2.062 (1.357-3.131)], ADC_H_GLRLM_mean [HR: 2.174 (1.457-3.244)], and ADC_O_GLCM_cluster_shade [HR: 1.882 (1.324-2.674)] were identified as representative prognostic indicators. The optimum threshold levels for these three features were 1.19×100, 1.71×10 and 2.19×0.1, respectively. Neither histogram analysis values nor fractal features revealed significant correlation with survival status (p > 0.05). CONCLUSIONS: Texture features of the mGIST on DWI exhibited correlation with overall survival. High-grade heterogeneity was associated with poor prognosis.