Multiscale Multiparametric
MRI
Deep Learning for Short‐Term Survival Assessment in Glioblastoma
Hongbo Zhang, Beibei Zhou, Xinzhu Zhao, Siqing Jing, Hanwen Zhang, Biao Huang, Zhongxian Yang, Jinhua Wang, Yubao Liu ABSTRACT
Background
Preoperative identification of short‐term survival in glioblastoma may guide management but remains challenging because of clinical and imaging heterogeneity.
Purpose
To develop and externally validate a multiscale magnetic resonance imaging (MRI)‐based deep learning model for short‐term survival assessment and explore transcriptomic correlates.
Study Type
Retrospective, multicenter.
Population
Adults with pathologically confirmed, newly diagnosed glioblastoma ( n = 728): training cohort ( n = 290; median age, 55 years; 169 men) and external cohorts 1–3 ( n = 225/182/31; median ages, 64/61/56 years; 136/108/20 men, respectively).
Field Strength/Sequence
1.5 T or 3.0 T; axial precontrast T1‐weighted, T2‐weighted, T2‐weighted fluid‐attenuated inversion recovery, and postcontrast T1‐weighted MRI.
Assessment
Short‐term survival was overall survival of 9 months or less. Whole‐brain, three‐dimensional tumor, and 2.5‐dimensional tumor inputs were integrated and compared with clinical, conventional MRI morphometric, and combined clinical‐MRI baselines.
Statistical Tests
Kruskal–Wallis, Mann–Whitney U /Wilcoxon rank‐sum, chi‐square, Fisher exact, DeLong, and log‐rank tests; Benjamini–Hochberg false discovery rate (FDR) correction; calibration, decision curves, edgeR, and correlation‐adjusted mean‐rank gene‐set testing were used. Areas under the receiver operating characteristic curve (AUCs) with 95% confidence intervals (CIs) summarized discrimination; threshold metrics used the Youden index. Two‐sided p < 0.05 or FDR‐adjusted p < 0.05 indicated significance.
Results
Apparent training AUC was 0.870 (95% CI: 0.830, 0.910); external AUCs were 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). AUC gains over the combined clinical‐MRI morphometric baseline were 0.161 and 0.131 in external cohorts 1 and 2; only cohort 1 remained significant after FDR correction (cohort 2, FDR‐adjusted p = 0.0897). Immune/inflammatory and cell‐division/genome‐maintenance pathway associations were directionally concordant, significant after FDR correction in both cohorts, and leave‐one‐out consistent.
Data Conclusion
Multiscale MRI deep learning demonstrated favorable discrimination for short‐term survival; model output was associated with immune‐ and cell‐cycle‐related transcriptomic programs.
Evidence Level
3.
Technical Efficacy
Stage 2.