Keywords: Machine Learning/Artificial Intelligence, RadiomicsDetecting true tumor recurrence and treatment-related effects in glioma after treatment is crucial for patient managements and challenging via conventional MRI for differentiation. Radiomics can be used to access the details in the images in an objective way. We constructed models based on multiple modalities by using radiomics features of the postoperative enhanced and edematous regions to find key features for identifying true tumor recurrence. Features from CE-T1WI and enhanced regions have excellent classification performance, and the model of multimodality with whole regions is the best, which may aid clinicians in developing individualized treatment strategies.
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