Keywords: Diagnosis/Prediction, Radiomics
Motivation: Ependymoma (EP) is a prevalent intracranial tumor in children, with significant implications of its molecular subtypes on treatment outcomes. However, there is currently a lack of non-invasive methods to distinguish between these molecular subtypes of EP.
Goal(s): To develop an non-invasive method to distinguish between these molecular subtypes of EP.
Approach: MRI data from 43 ependymoma patients was analyzed in this study, with radiomic features and machine learning used to distinguish molecular subtypes.
Results: The AUC for the proposed model was 0.840 (0.723-0.957) in the training group and 0.900 (0.698-1.000) in the test group.
Impact: The proposed machine learning model effectively distinguishes between the molecular subtypes of ependymoma, showing strong performance and enhancing diagnostic accuracy, which is expected to provide valuable insights for clinical decision-making.
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