Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks

• URL: http://arxiv.org/abs/2602.11234v1
• Date: Wed, 11 Feb 2026 16:28:13 GMT
• Title: Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks
• Authors: Ankita Paul, Wenyi Wang,
• Abstract summary: TopoGBM is a learning framework designed to capture scanner-robust representations from 3D MRI.<n>Mechanistic interpretability analysis reveals that reconstruction residuals are highly localized to pathologically heterogeneous zones.
• Score: 3.120728330365825
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• TwinPurify: Purifying gene expression data to reveal tumor-intrinsic transcriptional programs via self-supervised learning [4.742294289533828]
  We introduce TwinPurify, a representation learning framework that adapts the Barlow Twins self-supervised objective.<n>Rather than resolving the bulk mixture into discrete cell-type fractions, TwinPurify instead learns continuous, high-dimensional tumor embeddings.
  arXiv  Detail & Related papers  (2026-01-26T16:11:34Z)
• Robust Computational Extraction of Non-Enhancing Hypercellular Tumor Regions from Clinical Imaging Data [0.0]
  We present a robust computational framework that generates probability maps of NEH regions from routine MRI data.<n>Our approach was validated against independent clinical markers.
  arXiv  Detail & Related papers  (2026-01-25T11:39:41Z)
• IMSAHLO: Integrating Multi-Scale Attention and Hybrid Loss Optimization Framework for Robust Neuronal Brain Cell Segmentation [0.6999740786886535]
  We propose a novel deep learning framework, IMSAHLO, for robust and adaptive neuronal segmentation.<n>Our framework achieves precision of 81.4%, macro F1 score of 82.7%, micro F1 score of 83.3%, and balanced accuracy of 99.5% on difficult dense and sparse cases.
  arXiv  Detail & Related papers  (2026-01-14T23:43:33Z)
• FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI [44.4791295950757]
  We develop an unsupervised anomaly detection (UAD) approach for brain MRI.<n>We conduct the first systematic frequency-domain analysis of pathological signatures.<n>We show that Frequency-Decomposition Preprocessing (FDP) framework can leverage frequency-domain reconstruction for simultaneous pathology suppression and anatomical preservation.
  arXiv  Detail & Related papers  (2025-11-17T02:40:14Z)
• Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRI [7.144319861722029]
  Unsupervised anomaly detection (UAD) presents a complementary alternative to supervised learning for brain tumor segmentation in MRI.<n>We propose a novel Multimodal Vision Transformer Autoencoder (MViT-AE) trained exclusively on healthy brain MRIs to detect and localize tumors.<n>Our method achieves clinically meaningful tumor localization, with lesion-wise Dice Similarity Coefficient of 0.437 (Whole Tumor), 0.316 (Tumor Core), and 0.350 (Enhancing Tumor) on the test set, and an anomaly Detection Rate of 89.4% on the validation set.
  arXiv  Detail & Related papers  (2025-10-17T14:26:30Z)
• DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights [54.87947751720332]
  Accurate brain tumor segmentation is significant for clinical diagnosis and treatment.<n>Mamba-based State Space Models have demonstrated promising performance.<n>We propose a dual-resolution bi-directional Mamba that captures multi-scale long-range dependencies with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-10-16T07:31:21Z)
• Topology-Constrained Learning for Efficient Laparoscopic Liver Landmark Detection [46.2391319253146]
  Liver landmarks provide crucial anatomical guidance to the surgeon during laparoscopic liver surgery.<n>TopoNet is a novel topology-constrained learning framework for laparoscopic liver landmark detection.<n>Our framework adopts a snake-CNN dual-path encoder to simultaneously capture detailed RGB texture information and depth-informed topological structures.
  arXiv  Detail & Related papers  (2025-07-01T07:35:36Z)
• MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts [54.915060471994686]
  We propose MAST-Pro, a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation.<n>Specifically, text and anatomical prompts provide domain-specific priors guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning.<n>Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average improvement while reducing trainable parameters by 91.04%, without compromising accuracy.
  arXiv  Detail & Related papers  (2025-03-18T15:39:44Z)
• MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention [57.044719143401664]
  Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease.<n>We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention.<n>Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
  arXiv  Detail & Related papers  (2025-03-01T07:02:30Z)
• Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma [12.36330256366686]
  We propose an adaptive unsupervised learning approach for efficient MRI intra-tumor partitioning and glioblastoma survival prediction. A novel and problem-specific Feature-enhanced Auto-Encoder (FAE) is developed to enhance the representation of pairwise clinical modalities. The results demonstrate that the proposed approach can produce robust and clinically relevant MRI sub-regions and statistically significant survival predictions.
  arXiv  Detail & Related papers  (2021-08-21T02:47:59Z)
• Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI [47.26574993639482]
  We show improved anomaly segmentation performance and the general capability to obtain much more crisp reconstructions of input data at native resolution. The modeling of the laplacian pyramid further enables the delineation and aggregation of lesions at multiple scales.
  arXiv  Detail & Related papers  (2020-06-23T09:20:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.