Foundation Model Based on Routine Magnetic Resonance Imaging for Brain Tumor Molecular Profiling and Progression Prediction

UMBIF showed robust transferability to both post-therapy imaging assessment and molecular status prediction in glioma. By leveraging large-scale self-supervised pretraining to boost performance while reducing dependence on manual annotations, the framework may facilitate more efficient and reliable diagnostic workflows.

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Publication Details

Journal
JCO Precision Oncology
Published
2026-02-01
DOI
https://doi.org/10.1200/po-25-00930
Citations
1
Primary Topic
Glioma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
36.93
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article

Foundation Model Based on Routine Magnetic Resonance Imaging for Brain Tumor Molecular Profiling and Progression Prediction

Qian Su, Qiang Yin, Ximin Gao, Junxian Li et al.
1 citations
JCO Precision Oncology
Glioma Diagnosis and Treatment
36.93
article

Foundation Model Based on Routine Magnetic Resonance Imaging for Brain Tumor Molecular Profiling and Progression Prediction

Qian Su, Qiang Yin, Ximin Gao, Junxian Li, Yuchen Xing, Renhe Liu
article en
1 citations

Abstract

UMBIF showed robust transferability to both post-therapy imaging assessment and molecular status prediction in glioma. By leveraging large-scale self-supervised pretraining to boost performance while reducing dependence on manual annotations, the framework may facilitate more efficient and reliable diagnostic workflows.

JCO Precision OncologyVol. 10(2)
Tianjin Medical University Cancer Institute and Hospital (CN)
Peace, Justice and strong institutions, Reduced inequalities
Openalex Percentile: Top 1%
Glioma Diagnosis and Treatment
36.93
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