Yan Tan1,2, Shuai-Tong Zhang3,4, Jing-Wei Wei3,4, Di Dong3, Xiao-Chun Wang1,2, Guo-Qiang Yang1,2, Jie Tian5, Hui Zhang6,7. 1. Department of Radiology, The first hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China. 2. Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China. 3. Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China. 4. University of Chinese Academy of Sciences, Beijing, 100080, China. 5. Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China. tian@ieee.org. 6. Department of Radiology, The first hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China. zhanghui_mr@163.com. 7. Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China. zhanghui_mr@163.com.
Abstract
OBJECTIVES: To develop and validate a radiomics nomogram to preoperative prediction of isocitrate dehydrogenase (IDH) genotype for astrocytomas, which might contribute to the pretreatment decision-making and prognosis evaluating. METHODS: One hundred five astrocytomas (Grades II-IV) with contrast-enhanced T1-weighted imaging (CE-T1WI), T2 fluid-attenuated inversion recovery (T2FLAIR), and apparent diffusion coefficient (ADC) map were enrolled in this study (training cohort: n = 74; validation cohort: n = 31). IDH1/2 genotypes were determined using Sanger sequencing. A total of 3882 radiomics features were extracted. Support vector machine algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinico-radiological risk factors, the radiomics nomogram was developed. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess these models. Kaplan-Meier survival analysis and log rank test were performed to assess the prognostic value of the radiomics nomogram. RESULTS: The radiomics signature was built by six selected radiomics features and yielded AUC values of 0.901 and 0.888 in the training and validation cohorts. The radiomics nomogram based on the radiomics signature and age performed better than the clinico-radiological model (training cohort, AUC = 0.913 and 0.817; validation cohort, AUC = 0.900 and 0.804). Additionally, the survival analysis showed that prognostic values of the radiomics nomogram and IDH genotype were similar (log rank test, p < 0.001; C-index = 0.762 and 0.687; z-score test, p = 0.062). CONCLUSIONS: The radiomics nomogram might be a useful supporting tool for the preoperative prediction of IDH genotype for astrocytoma, which could aid pretreatment decision-making. KEY POINTS: • The radiomics signature based on multiparametric and multiregional MRI images could predict IDH genotype of Grades II-IV astrocytomas. • The radiomics nomogram performed better than the clinico-radiological model, and it might be an easy-to-use supporting tool for IDH genotype prediction. • The prognostic value of the radiomics nomogram was similar with that of the IDH genotype, which might contribute to prognosis evaluating.
OBJECTIVES: To develop and validate a radiomics nomogram to preoperative prediction of isocitrate dehydrogenase (IDH) genotype for astrocytomas, which might contribute to the pretreatment decision-making and prognosis evaluating. METHODS: One hundred five astrocytomas (Grades II-IV) with contrast-enhanced T1-weighted imaging (CE-T1WI), T2 fluid-attenuated inversion recovery (T2FLAIR), and apparent diffusion coefficient (ADC) map were enrolled in this study (training cohort: n = 74; validation cohort: n = 31). IDH1/2 genotypes were determined using Sanger sequencing. A total of 3882 radiomics features were extracted. Support vector machine algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinico-radiological risk factors, the radiomics nomogram was developed. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess these models. Kaplan-Meier survival analysis and log rank test were performed to assess the prognostic value of the radiomics nomogram. RESULTS: The radiomics signature was built by six selected radiomics features and yielded AUC values of 0.901 and 0.888 in the training and validation cohorts. The radiomics nomogram based on the radiomics signature and age performed better than the clinico-radiological model (training cohort, AUC = 0.913 and 0.817; validation cohort, AUC = 0.900 and 0.804). Additionally, the survival analysis showed that prognostic values of the radiomics nomogram and IDH genotype were similar (log rank test, p < 0.001; C-index = 0.762 and 0.687; z-score test, p = 0.062). CONCLUSIONS: The radiomics nomogram might be a useful supporting tool for the preoperative prediction of IDH genotype for astrocytoma, which could aid pretreatment decision-making. KEY POINTS: • The radiomics signature based on multiparametric and multiregional MRI images could predict IDH genotype of Grades II-IV astrocytomas. • The radiomics nomogram performed better than the clinico-radiological model, and it might be an easy-to-use supporting tool for IDH genotype prediction. • The prognostic value of the radiomics nomogram was similar with that of the IDH genotype, which might contribute to prognosis evaluating.
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