Peng Lin1, Yu-Ting Peng1, Rui-Zhi Gao1, Yan Wei2, Xiao-Jiao Li3, Su-Ning Huang4, Ye-Ying Fang5, Zhu-Xin Wei5, Zhi-Guang Huang2, Hong Yang1, Gang Chen6. 1. Department of Ultrasound, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China. 2. Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China. 3. Department of PET-CT, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China. 4. Department of Radiotherapy, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China. 5. Department of Radiotherapy, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China. 6. Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China. chen_gang_triones@163.com.
Abstract
PURPOSE: To evaluate a radiomic approach for the stratification of diffuse gliomas with distinct prognosis and provide additional resolution of their clinicopathological and molecular characteristics. METHODS: For this retrospective study, a total of 704 radiomic features were extracted from the multi-channel MRI data of 166 diffuse gliomas. Survival-associated radiomic features were identified and submitted to distinguish glioma subtypes using consensus clustering. Multi-layered molecular data were used to observe the different clinical and molecular characteristics between radiomic subtypes. The relative profiles of an array of immune cell infiltrations were measured gene set variation analysis approach to explore differences in tumor immune microenvironment. RESULTS: A total of 6 categories, including 318 radiomic features were significantly correlated with the overall survival of glioma patients. Two subgroups with distinct prognosis were separated by consensus clustering of radiomic features that significantly associated with survival. Histological stage and molecular factors, including IDH status and MGMT promoter methylation status were significant differences between the two subtypes. Furthermore, gene functional enrichment analysis and immune infiltration pattern analysis also hinted that the inferior prognosis subtype may more response to immunotherapy. CONCLUSION: A radiomic model derived from multi-parameter MRI of the gliomas was successful in the risk stratification of diffuse glioma patients. These data suggested that radiomics provided an alternative approach for survival estimation and may improve clinical decision-making.
PURPOSE: To evaluate a radiomic approach for the stratification of diffuse gliomas with distinct prognosis and provide additional resolution of their clinicopathological and molecular characteristics. METHODS: For this retrospective study, a total of 704 radiomic features were extracted from the multi-channel MRI data of 166 diffuse gliomas. Survival-associated radiomic features were identified and submitted to distinguish glioma subtypes using consensus clustering. Multi-layered molecular data were used to observe the different clinical and molecular characteristics between radiomic subtypes. The relative profiles of an array of immune cell infiltrations were measured gene set variation analysis approach to explore differences in tumor immune microenvironment. RESULTS: A total of 6 categories, including 318 radiomic features were significantly correlated with the overall survival of gliomapatients. Two subgroups with distinct prognosis were separated by consensus clustering of radiomic features that significantly associated with survival. Histological stage and molecular factors, including IDH status and MGMT promoter methylation status were significant differences between the two subtypes. Furthermore, gene functional enrichment analysis and immune infiltration pattern analysis also hinted that the inferior prognosis subtype may more response to immunotherapy. CONCLUSION: A radiomic model derived from multi-parameter MRI of the gliomas was successful in the risk stratification of diffuse gliomapatients. These data suggested that radiomics provided an alternative approach for survival estimation and may improve clinical decision-making.
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