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Abstract #1286

Diffusion-Weighted MRI Radiomics Model for Differentiating High-Grade Gliomas in Non-Enhancing (Low-Grade Appearance) Gliomas

Yanhong Liu1, Yuhan Liang1, Zelong Chen1, and Yulin Wang1
1Chinese PLA General Hospital, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Radiomics

Motivation: Approximately 8% to 18% of non-enhancing(non-CE) gliomas ultimately diagnosed as glioblastoma lack contrast enhancement and exhibit a 'low-grade appearance' on imaging. This may lead to misdiagnosis, treatment delays, and adverse patient outcomes.

Goal(s): Apply radiomics to build a preoperative predictive model and assess its value in differentiating non-CE high-grade gliomas preoperatively.

Approach: The T2WI, T1CE and ADC sequences of patients with pathologically confirmed glioma were retrospectively analyzed. Diagnostic performance was evaluated using the AUC.

Results: The multi-parametric sequence (T2WI + T1CE + ADC) achieved the highest AUC, with an AUC of 0.892,in the training set and 0.805 in the validation set.

Impact: The radiomics model based on diffusion MRI can non-invasively differentiate between high-grade and low-grade gliomas in non-CE adult diffuse gliomas,providing valuable support for decision-making in clinical surgical strategies and prognostic management.

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Keywords