CORRELATION OF MRI-BASED RADIOMIC FEATURES WITH HISTOPATHOLOGICAL GRADE AND MOLECULAR MARKERS IN HIGH-GRADE GLIOMAS
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Keywords
High-grade glioma, magnetic resonance imaging, radiomics, histopathology, IDH mutation, MGMT promoter methylation, apparent diffusion coefficient
Abstract
Background: High-grade gliomas are aggressive primary brain tumours associated with considerable morbidity and mortality. Their diagnosis and classification increasingly depend on the integration of magnetic resonance imaging (MRI), histopathological findings, and molecular markers. MRI-based radiomics provides quantitative information regarding tumour intensity, shape, texture, and heterogeneity that may help predict tumour grade and molecular characteristics.
Aim: To evaluate the correlation between MRI-based radiomic features, histopathological grade, and molecular markers in patients with high-grade gliomas.
Materials and Methods: This observational study included 100 patients with high-grade gliomas. MRI findings were evaluated for conventional imaging characteristics and quantitative radiomic parameters, including tumour volume, entropy, grey-level co-occurrence matrix contrast, homogeneity, and apparent diffusion coefficient values where available. Histopathological grading and molecular marker results were obtained from the corresponding pathological records. Associations between radiomic features, histopathological grade, and molecular markers were assessed using appropriate statistical tests.
Results: A total of 100 patients were included in the study. In this study of 100 cases, 14 (14%) were positive and 86 (86%) negative. Entropy correlated positively with Ki-67 (rs=0.58r_s=0.58rs=0.58), while mean ADC showed a negative correlation (rs=−0.62r_s=-0.62rs=−0.62); tumour volume correlated positively with histopathological grade (rs=0.46r_s=0.46rs=0.46). In the ROC analysis, the AUC was 0.72 for tumour volume, 0.81 for entropy, 0.75 for GLCM contrast, 0.84 for mean ADC, and 0.89 for the combined radiomic model.
Conclusion: MRI-based radiomics may provide quantitative information complementary to conventional MRI and histopathological assessment in high-grade gliomas. Its potential role in predicting molecular characteristics and tumour grade requires evaluation using reproducible feature extraction methods, appropriate statistical analysis, and independent validation.
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