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Abstract #0086

Optimizing Glioma Classification: Comparative Analysis of Radiologist, Radiomics and Multi-dimensional Deep Learning Using Quantitative MRI

Xin Ge1, Yuhui Xiong2, and Jing Zhang3
1Second Clinical School, Lanzhou University, Lanzhou, China, 2GE Healthcare MR Research, Beijing, China, Beijing, China, 3Lanzhou University Second Hospital, Lanzhou, China

Synopsis

Keywords: Tumors (Pre-Treatment), Tumors

Motivation: Gliomas present complex challenges in neuro-oncology, necessitating improved diagnostic methods that incorporate molecular characteristics alongside traditional imaging.

Goal(s): The research aims to optimize adult diffuse glioma classification by integrating advanced quantitative MRI techniques with radiomics and DL models, seeking to surpass conventional radiologist assessments.

Approach: A prospective study involving 428 patients employed advanced imaging protocols, including MUSE-DWI, 3D-pCASL, and synthetic MRI, combined with the development of independent binary classifiers for IDH mutation and 1p/19q co-deletion classification.

Results: The 2.5D-DL fusion model demonstrated the highest diagnostic performance, significantly outperforming traditional methods and showcasing the effectiveness of multimodal imaging in glioma classification.

Impact: This study could significantly enhance glioma diagnostics by employing advanced quantitative MRI alongside DL models, such as 2.5D-DL fusion model, which demonstrates superior classification accuracy. This approach enables personalized treatment strategies based on molecular insights, previously unattainable with traditional methods.

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Keywords