Robust deep learning for incomplete MRI sequences in glioma grading and IDH mutation status prediction: a large-scale multicenter study.

doi:10.1007/s00330-026-12622-4

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Article

Robust deep learning for incomplete MRI sequences in glioma grading and IDH mutation status prediction: a large-scale multicenter study.

European radiology - 1 Oct 2026

Liang Fangrong, Yan Jing, Lai Shengsheng, Zhang Chaoli, Lin Jiaxin, Wei Ruili, Wu Yuankui, Cheng Jingliang, Liu Yaou, Wang Wensheng, Zhen Xin, Yang Ruimeng

Abstract excerpt

OBJECTIVES: Incomplete MRI sequences pose a significant challenge to the reliability of multiparametric MRI (mp-MRI) radiomics models. This study aimed to develop a robust and noninvasive approach for accurate glioma grading and isocitrate dehydrogenase (IDH) mutation status prediction using incomplete MRI data. MATERIALS AND METHODS: Conventional MRI scans of 2170 glioma patients were retrospectively collected...

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Topics

Humans
Glioma
Isocitrate Dehydrogenase
Brain Neoplasms
Neoplasm Grading
Mutation

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Magnetic Resonance Imaging

Radiomics

Deep Learning

Retrospective Studies

Female

Male

Middle Aged

Reproducibility of Results

Adult

Source

Open in PubMed

Identifiers

PMID: 42142114
DOI: 10.1007/s00330-026-12622-4

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