Related Experiment Video
Updated: Aug 5, 2026
Live-3D-Cell Immunocytochemistry Assays of Pediatric Diffuse Midline Glioma
Published on: November 11, 2021
Uncertainty-Aware Risk Stratification in Pediatric Low-Grade Glioma Using Multimodal Data
Fadel Batal1, Bhavyasri Vunnava1, Adam Kraya1
1From the Center for Data-Driven Discovery in Biomedicine (D3b) (F.B., B.V., A.K., D.G., A.F., K.R., S.V., D.C., S.R., O.F., A.K., A.B., P.B.S., A.R., A.V., A.N., A.F.K.), Neurosurgery (P.B.S., A.R., A.F.K.), Radiology (A.V.), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Bioengineering (F.B., D.C., A.F.K.), Neurosurgery (P.B.S., A.R., A.F.K.), Perelman School of Medicine, Radiology (A.V., A.N.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA and Transitional Year Residency Program, Southeast Health Medical Center, Dothan, AL, USA.
Background And Purpose:
Risk stratification in pediatric low-grade glioma (pLGG) remains challenging due to biological and clinical heterogeneity. We developed an uncertainty-aware multimodal survival framework that integrates deep learning features from T2-weighted MRI, molecular subtype, and clinical information.
Materials And Methods:
Data from the Children's Brain Tumor Network included 360 subjects with imaging data and 493 with molecular subtype information derived from tumor tissue genomic profiling; clinical data were available for all patients. A pretrained deep learning model was fine-tuned for tumor segmentation using a pediatric brain tumor cohort (n=752) and subsequently used to extract imaging features from T2-weighted MRI. A regularized survival model integrating imaging and clinical features (clinico-ResNet; DL-M1) and a separate clinical-molecular survival model were trained and validated on discovery cohorts and evaluated on independent replication cohorts. A multimodal model (DL-M2), combining risk scores from the clinico-ResNet and clinical-molecular models through late fusion, was developed in the subsect of patients with both imaging and molecular data (n=294). Bootstrap resampling was used to quantify per-patient prediction uncertainty.
Results:
DL-M1 achieved Harrell's C-indices of 0.73 (95% CI: 0.68-0.78) and 0.70 in the discovery and replication cohorts, respectively, with performance statistically comparable to a multiparametric radiomic pipeline (p>0.05). Adding molecular subtype information improved performance in the replication cohort (C-index: 0.68 vs 0.63, p=0.016) but not in the discovery cohort (0.79 vs 0.78, p=0.26). The multimodal model reclassified 18 patients in a manner consistent with established BRAF-associated prognostic biology. Uncertainty analysis showed a marked reduction in bootstrap confidence interval widths following late fusion with the molecular model, decreasing from a median of 2.37 to 0.57 in the discovery cohort and from 2.32 to 0.60 in the replication cohort, corresponding to relative reductions of 75.8% and 75.2%, respectively.
Conclusions:
Integrating molecular subtype into a clinical-imaging survival framework improved risk stratification in pLGG. Deep learning features extracted from T2-weighted MRI achieved performance comparable to a multiparametric radiomics pipeline without complex tumor segmentation. These findings support uncertainty-aware multimodal survival modeling as a streamlined and more transparent approach for pLGG risk stratification.