Multiparametric Quantitative MRI With 2.5D Deep Learning for Molecular Classification and Overall Survival Assessment in Adult-Type Diffuse Gliomas
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Xin Ge1,2,3, Ying Shen4, Yu Han1,2,3
1Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Background:
Quantitative MRI may enable noninvasive glioma characterization, but optimal analytic strategies and prognostic utility remain uncertain.
Purpose:
To compare volume-of-interest (VOI)-mean, radiomics, and 2D, 2.5D, and 3D deep learning (DL) for preoperative molecular classification and assess the prognostic value of a 2.5D-DL-derived isocitrate dehydrogenase wild-type score (2.5D-IDHwt score) in a postoperative clinicopathologic survival model.
Study Type:
Retrospective multicenter study.
Population:
One thousand four hundred and eight adults with diffuse glioma: training (n = 409), temporal validation (n = 300), and three external test cohorts (n = 119, 310, and 270).
Field Strength/Sequence:
3 T; spin-echo echo-planar diffusion-weighted imaging, 3D pseudo-continuous arterial spin labeling, and multidynamic multiecho synthetic MRI.
Assessment:
Apparent diffusion coefficient, cerebral blood flow, T1, T2, proton-density, and contrast-enhanced T1 maps were generated. Two neuroradiologists (5 and 20 years' experience) delineated whole-tumor VOIs. Six map-specific models were stacked separately for VOI-mean, radiomics, 2D-DL, 2.5D-DL, and 3D-DL. 2.5D-qMR-Clin combined the 2.5D-IDHwt score with age, O6-methylguanine-DNA methyltransferase promoter methylation, and extent of resection and was compared with PathoClin incorporating pathological IDH status.
Statistical Tests:
Area under the curve (AUC), DeLong tests, Harrell C-index, bootstrap resampling, calibration, decision-curve analysis, Kaplan-Meier analysis, and Cox regression. p < 0.05 indicated significance.
Results:
The 2.5D-DL stacking model achieved the highest external AUCs for IDH mutation (0.89-0.93) and 1p/19q codeletion (0.92-0.95), significantly outperforming the other four stacking models. Reverse Kaplan-Meier median follow-up was 1344 days (95% CI, 1316-1383); 27.5% died. External C-indices for 2.5D-qMR-Clin were 0.750, 0.778, and 0.762 versus 0.704, 0.722, and 0.723 for PathoClin, with significant differences in test cohorts 2 and 3. Fixed-cutoff risk stratification was significant across all external cohorts (hazard ratios, 5.30-5.80). A web-based 2.5D-qMR-Clin explorer provided 1-, 2-, and 3-year OS estimates.
Data Conclusion:
Among evaluated approaches, 2.5D-DL stacking performed best for molecular classification, and the 2.5D-IDHwt score improved OS assessment in the postoperative clinicopathologic model.
Evidence Level:
3.
Technical Efficacy:
Stage 2.