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Abstract #0220

Leveraging the Untapped Potentials of Incomplete MRI Sequences for Glioma Grading and IDH Mutation Status Prediction

Fangrong Liang1, Jing Yan2, Jiaxin Lin1, Ruili Wei1, Yongzhou Xu3, Xin Zhen4, and Ruimeng Yang1
1Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China, 2Department of MRI, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China, 3Philips Healthcare, Guangzhou, China, 4School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Synopsis

Keywords: Diagnosis/Prediction, Radiomics, glioma;MRI

Motivation: The absence of MRI sequences poses significant challenges to reliable predictive modeling in multiparametric magnetic resonance imaging (mp-MRI) radiomics for glioma grading and predicting isocitrate dehydrogenase (IDH) mutation status.

Goal(s): This study developed a disentangled-learning-based incomplete sequence completion enhanced robust network (DISCERN) to impute missing features and learn latent fusion representations, which are used for accurate, noninvasive glioma grading and IDH status prediction.

Approach: Validation was performed across multi-center datasets and simulations of various clinical scenarios with differing missing rates to assess DISCERN's resilience to incomplete sequences.

Results: DISCERN achieved robust performance in both glioma grading and IDH prediction.

Impact: The DISCERN model demonstrates significant potentials for real-world clinical applications in noninvasive glioma grading and IDH mutation status prediction with incomplete mp-MRI data, offering a robust tool for clinical decision-making and personalized treatment planning.

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