Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype

2026-01-16

AI Summary

This study addresses the challenge of automatically extracting interpretable prognostic morphological features from whole-slide images of IDH wild-type glioblastoma to predict patient survival. We propose a novel interpretable multiple instance learning framework that, for the first time, integrates a sparse autoencoder with a Cox proportional hazards model. Evaluated on 720 real-world cases, the model achieves an AUC of 0.67 in survival stratification. It identifies 24 visual patches significantly associated with survival, 21 of which were validated by neuropathologists and categorized into seven distinct histological feature classes. This approach enables an automatic mapping from raw histopathology images to clinically comprehensible morphological patterns, substantially enhancing model transparency and pathological relevance.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Glioblastoma, IDH-wildtype (GBM-IDHwt) is the most common malignant brain tumor. Histomorphology is a crucial component of the integrated diagnosis of GBM-IDHwt. Artificial intelligence (AI) methods have shown promise to extract additional prognostic information from histological whole-slide images (WSI) of hematoxylin and eosin-stained glioblastoma tissue. Here, we present an explainable AI-based method to support systematic interpretation of histomorphological features associated with survival. It combines an explainable multiple instance learning (MIL) architecture with a sparse autoencoder (SAE) to relate human-interpretable visual patterns of tissue to survival. The MIL architecture directly identifies prognosis-relevant image tiles and the SAE maps these tiles post-hoc to visual patterns. The MIL method was trained and evaluated using a new real-world dataset that comprised 720 GBM-IDHwt cases from three hospitals and four cancer registries in Germany. The SAE was trained using 1878 WSIs of glioblastoma from five independent public data collections. Despite the many factors influencing survival time, our method showed some ability to discriminate between patients living less than 180 days or more than 360 days solely based on histomorphology (AUC: 0.67; 95% CI: 0.63-0.72). Cox proportional hazards regression confirmed a significant difference in survival time between the predicted groups after adjustment for established prognostic factors (hazard ratio: 1.47; 95% CI: 1.26-1.72). Our method identified multiple interpretable visual patterns associated with survival. Three neuropathologists separately found that 21 of the 24 most strongly associated patterns could be clearly attributed to seven histomorphological categories. Necrosis and hemorrhage appeared to be associated with shorter survival while highly cellular tumor areas were associated with longer survival.

Problem

• glioblastoma
• histomorphology
• survival prediction
• explainable AI
• whole-slide image

Innovation

• explainable AI
• multiple instance learning
• sparse autoencoder
• histomorphology
• glioblastoma survival prediction