Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| author | Redlich, Jan-Philipp Feuerhake, Friedrich Nikolin, Stefan Schaadt, Nadine Sarah Teuber-Hanselmann, Sarah Weis, Joachim Luttmann, Sabine Eberle, Andrea Buck, Christoph Intemann, Timm Birnstill, Pascal Kraywinkel, Klaus Ort, Jonas Boor, Peter Homeyer, André |
| author_facet | Redlich, Jan-Philipp Feuerhake, Friedrich Nikolin, Stefan Schaadt, Nadine Sarah Teuber-Hanselmann, Sarah Weis, Joachim Luttmann, Sabine Eberle, Andrea Buck, Christoph Intemann, Timm Birnstill, Pascal Kraywinkel, Klaus Ort, Jonas Boor, Peter Homeyer, André |
| contents | Glioblastoma, IDH-wildtype (GBM-IDHwt) is the most common malignant brain tumor. While histomorphology is a crucial component of GBM-IDHwt diagnosis, it is not further considered for prognosis. Here, we present an explainable artificial intelligence (AI) framework to identify and interpret histomorphological features associated with patient survival. The framework combines an explainable multiple instance learning (MIL) architecture that directly identifies prognostically relevant image tiles with a sparse autoencoder (SAE) that maps these tiles to interpretable visual patterns. The MIL model was trained and evaluated on a new real-world dataset of 720 GBM-IDHwt cases from three hospitals and four cancer registries across Germany. The SAE was trained on 1,878 whole-slide images from five independent public glioblastoma 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 survival difference between predicted groups after adjustment for established prognostic factors (hazard ratio: 1.47; 95% CI: 1.26-1.72). Three neuropathologists categorized the identified visual patterns into seven distinct histomorphological groups, revealing both established prognostic features and unexpected associations, the latter being potentially attributable to surgery-related confounders. The presented explainable AI framework facilitates prognostic biomarker discovery in GBM-IDHwt and beyond, highlighting promising histomorphological features for further analysis and exposing potential confounders that would be hidden in black-box models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_11691 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype Redlich, Jan-Philipp Feuerhake, Friedrich Nikolin, Stefan Schaadt, Nadine Sarah Teuber-Hanselmann, Sarah Weis, Joachim Luttmann, Sabine Eberle, Andrea Buck, Christoph Intemann, Timm Birnstill, Pascal Kraywinkel, Klaus Ort, Jonas Boor, Peter Homeyer, André Image and Video Processing Machine Learning Quantitative Methods Glioblastoma, IDH-wildtype (GBM-IDHwt) is the most common malignant brain tumor. While histomorphology is a crucial component of GBM-IDHwt diagnosis, it is not further considered for prognosis. Here, we present an explainable artificial intelligence (AI) framework to identify and interpret histomorphological features associated with patient survival. The framework combines an explainable multiple instance learning (MIL) architecture that directly identifies prognostically relevant image tiles with a sparse autoencoder (SAE) that maps these tiles to interpretable visual patterns. The MIL model was trained and evaluated on a new real-world dataset of 720 GBM-IDHwt cases from three hospitals and four cancer registries across Germany. The SAE was trained on 1,878 whole-slide images from five independent public glioblastoma 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 survival difference between predicted groups after adjustment for established prognostic factors (hazard ratio: 1.47; 95% CI: 1.26-1.72). Three neuropathologists categorized the identified visual patterns into seven distinct histomorphological groups, revealing both established prognostic features and unexpected associations, the latter being potentially attributable to surgery-related confounders. The presented explainable AI framework facilitates prognostic biomarker discovery in GBM-IDHwt and beyond, highlighting promising histomorphological features for further analysis and exposing potential confounders that would be hidden in black-box models. |
| title | Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype |
| topic | Image and Video Processing Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2601.11691 |