Adult Supratentorial Extraventricular Ependymoma (STEE) are rare neoplasms that are often misdiagnosed as high-grade gliomas (HGG) due to their similar radiological manifestation on MRI. However, the pathogenesis and treatment plan of ependymoma differs significantly from gliomas, and hence an early and accurate diagnosis is crucial. We propose a novel machine learning based diagnostic model that can accurately distinguish adult STEE from HGG subtypes using quantitative radiomic signatures from a multi-model MRI data. First order and texture based radiomic features, particularly from FLAIR, T2 and ADC, can capture intricate pathological variations and aid in accurate and differential diagnosis of STEE tumors.
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