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Published on: June 7, 2020
Multiscale Multiparametric MRI Deep Learning for Short-Term Survival Assessment in Glioblastoma
Hongbo Zhang1,2, Beibei Zhou3, Xinzhu Zhao1,2
1Medical Image Center, Shenzhen Hospital, Southern Medical University (Shenzhen School of Clinical Medicine, Southern Medical University), Shenzhen, Guangdong, China.
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.
