ArticleSurgical neurology international2025
Assessing the predictability of isocitrate dehydrogenase mutational status in glioma patients using imaging features.
Article in Surgical neurology international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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6 authors.
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Abstract
Background: Radiogenomics, the intersection of imaging and genetics, is important in improving glioma diagnosis and treatment. This study aims to correlate imaging features with isocitrate dehydrogenase (IDH) mutation status, providing a non-invasive diagnostic tool to identify the genetic background of gliomas. Methods: In a retrospective sample of 59 patients with either IDH wild-type (WT) or IDH mutant gliomas, the study employed volumetric and morphologic magnetic resonance imaging (MRI) analyses to discern molecular alterations based on radiographic signatures. Key imaging biomarkers, such as the T2/fluid-attenuated inversion recovery mismatch, contrast enhancement patterns, and diffusion/perfusion metrics, were evaluated for their ability to differentiate between IDH-WT and mutant gliomas. Receiver operating characteristic curves were employed to evaluate diagnostic performance, and a logistic regression model was developed for patient classification based on imaging. Results: The results revealed that IDH mutant gliomas frequently exhibited distinct imaging characteristics, such as homogenous hyperintense T2 signals and absence of contrast enhancement. In addition, perfusion and diffusion metrics varied significantly between the IDH-WT and mutant groups, offering potential radiogenomic markers. A logistic regression model was developed to predict IDH status with high accuracy, identifying factors such as tumor enhancement size, presence of central necrosis, peritumoral edema, and patient age. Conclusion: The study's results affirm the significance of radiogenomic correlations in predicting IDH status, resonating findings from prior research. We highlight the necessity of a multimodal approach in MRI analysis to enhance the non-invasive diagnostic accuracy for glioma patients.
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