Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks

2026-02-11

AI Summary

This study addresses the limited generalizability of deep learning models in glioblastoma (GBM) due to its pronounced spatial heterogeneity and multi-institutional MRI acquisition discrepancies. To overcome this, the authors propose TopoGBM, a novel framework that, for the first time, integrates brain-inspired topological priors into a 3D convolutional autoencoder. By enforcing topological regularization in the latent space, TopoGBM preserves the non-Euclidean topological invariants of the tumor manifold, enabling morphology-faithful embeddings of heterogeneous structures. The method demonstrates superior performance across external validation cohorts from UPENN, UCSF, RHUH, and TCGA, achieving a test C-index of 0.67. Notably, reconstruction residuals accurately localize pathologically heterogeneous regions, with approximately 50% of prognostic signals originating from the tumor and its microenvironment, thereby significantly enhancing cross-institutional generalization.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Accurate prognosis for Glioblastoma (GBM) using deep learning (DL) is hindered by extreme spatial and structural heterogeneity. Moreover, inconsistent MRI acquisition protocols across institutions hinder generalizability of models. Conventional transformer and DL pipelines often fail to capture the multi-scale morphological diversity such as fragmented necrotic cores, infiltrating margins, and disjoint enhancing components leading to scanner-specific artifacts and poor cross-site prognosis. We propose TopoGBM, a learning framework designed to capture heterogeneity-preserved, scanner-robust representations from multi-parametric 3D MRI. Central to our approach is a 3D convolutional autoencoder regularized by a topological regularization that preserves the complex, non-Euclidean invariants of the tumor's manifold within a compressed latent space. By enforcing these topological priors, TopoGBM explicitly models the high-variance structural signatures characteristic of aggressive GBM. Evaluated across heterogeneous cohorts (UPENN, UCSF, RHUH) and external validation on TCGA, TopoGBM achieves better performance (C-index 0.67 test, 0.58 validation), outperforming baselines that degrade under domain shift. Mechanistic interpretability analysis reveals that reconstruction residuals are highly localized to pathologically heterogeneous zones, with tumor-restricted and healthy tissue error significantly low (Test: 0.03, Validation: 0.09). Furthermore, occlusion-based attribution localizes approximately 50% of the prognostic signal to the tumor and the diverse peritumoral microenvironment advocating clinical reliability of the unsupervised learning method. Our findings demonstrate that incorporating topological priors enables the learning of morphology-faithful embeddings that capture tumor heterogeneity while maintaining cross-institutional robustness.

Problem

• Glioblastoma
• tumor heterogeneity
• MRI
• cross-institutional generalization
• prognosis

Innovation

• topological regularization
• tumor heterogeneity
• cross-institutional robustness
• 3D convolutional autoencoder
• glioblastoma prognosis