What question did this study set out to answer?

To develop and externally validate a multiscale multiparametric MRI-based deep learning model for predicting short-term survival in newly diagnosed glioblastoma and investigate its transcriptomic correlates.

August 16, 2026Journal of Magnetic Resonance Imaging

Multiscale Multiparametric MRI Deep Learning for Short‐Term Survival Assessment in Glioblastoma

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Authors

HZHongbo Zhang BZBeibei Zhou XZXinzhu Zhao

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Overview

Retrospective multicenter study demonstrates multiscale MRI deep learning predicts short-term survival in glioblastoma, highlighting links to immune and cell-cycle pathways.

Key Points

To develop and externally validate a multiscale multiparametric MRI-based deep learning model for predicting short-term survival in newly diagnosed glioblastoma and investigate its transcriptomic correlates.
Retrospective, multicenter study of 728 adults with pathologically confirmed, newly diagnosed glioblastoma, split into a training cohort (n = 290) and three external validation cohorts (n = 225, 182, and 31).
Model inputs integrated whole-brain, 3D tumor, and 2.5D tumor features across 1.5 T/3.0 T axial precontrast T1, T2, FLAIR, and postcontrast T1 sequences, evaluated against clinical and conventional MRI baselines.
The deep learning model achieved an apparent training AUC of 0.870 (95% CI: 0.830, 0.910) and external validation AUCs of 0.871 (95% CI: 0.821, 0.920), 0.828 (95% CI: 0.761, 0.895), and 0.798 (95% CI: 0.640, 0.956) for predicting survival ≤ 9 months.
The model yielded AUC gains over combined clinical-MRI morphometric baselines of 0.161 (FDR-adjusted p < 0.05) in external cohort 1 and 0.131 (FDR-adjusted p = 0.0897) in external cohort 2, showing significant concordant associations with immune, inflammatory, and cell-division pathways.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a819d36f2fb91fc834aece8 https://doi.org/10.1002/jmri.70497

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August 16, 2026Journal of Magnetic Resonance Imaging

Multiscale Multiparametric MRI Deep Learning for Short‐Term Survival Assessment in Glioblastoma

View Full Paper

Ask AI

Bookmark

Authors

HZHongbo Zhang BZBeibei Zhou XZXinzhu Zhao

Discussion

Member takes

Overview

Retrospective multicenter study demonstrates multiscale MRI deep learning predicts short-term survival in glioblastoma, highlighting links to immune and cell-cycle pathways.

Key Points

To develop and externally validate a multiscale multiparametric MRI-based deep learning model for predicting short-term survival in newly diagnosed glioblastoma and investigate its transcriptomic correlates.
Retrospective, multicenter study of 728 adults with pathologically confirmed, newly diagnosed glioblastoma, split into a training cohort (n = 290) and three external validation cohorts (n = 225, 182, and 31).
Model inputs integrated whole-brain, 3D tumor, and 2.5D tumor features across 1.5 T/3.0 T axial precontrast T1, T2, FLAIR, and postcontrast T1 sequences, evaluated against clinical and conventional MRI baselines.
The deep learning model achieved an apparent training AUC of 0.870 (95% CI: 0.830, 0.910) and external validation AUCs of 0.871 (95% CI: 0.821, 0.920), 0.828 (95% CI: 0.761, 0.895), and 0.798 (95% CI: 0.640, 0.956) for predicting survival ≤ 9 months.
The model yielded AUC gains over combined clinical-MRI morphometric baselines of 0.161 (FDR-adjusted p < 0.05) in external cohort 1 and 0.131 (FDR-adjusted p = 0.0897) in external cohort 2, showing significant concordant associations with immune, inflammatory, and cell-division pathways.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a819d36f2fb91fc834aece8 https://doi.org/10.1002/jmri.70497

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