The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information
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| Format: | Preprint |
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2025
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| author | Abler, D. Pusterla, O. Joye-Kühnis, A. Andratschke, N. Bach, M. Bink, A. Christ, S. M. Hagmann, P. Pouymayou, B. Pravatà, E. Radojewski, P. Reyes, M. Ruinelli, L. Schaer, R. Stieltjes, B. Treglia, G. Valenzuela, W. Wiest, R. Zoergiebel, S. Guckenberger, M. Tanadini-Lang, S. Depeursinge, A. |
| author_facet | Abler, D. Pusterla, O. Joye-Kühnis, A. Andratschke, N. Bach, M. Bink, A. Christ, S. M. Hagmann, P. Pouymayou, B. Pravatà, E. Radojewski, P. Reyes, M. Ruinelli, L. Schaer, R. Stieltjes, B. Treglia, G. Valenzuela, W. Wiest, R. Zoergiebel, S. Guckenberger, M. Tanadini-Lang, S. Depeursinge, A. |
| contents | Background: Radiomics shows promise in characterizing glioblastoma, but its added value over clinical and molecular predictors has yet to be proven. This study assessed the added value of conventional radiomics (CR) and deep learning (DL) MRI radiomics for glioblastoma prognosis (<= 6 vs > 6 months survival) on a large multi-center dataset.
Methods: After patient selection, our curated dataset gathers 1152 glioblastoma (WHO 2016) patients from five Swiss centers and one public source. It included clinical (age, gender), molecular (MGMT, IDH), and baseline MRI data (T1, T1 contrast, FLAIR, T2) with tumor regions. CR and DL models were developed using standard methods and evaluated on internal and external cohorts. Sub-analyses assessed models with different feature sets (imaging-only, clinical/molecular-only, combined-features) and patient subsets (S-1: all patients, S-2: with molecular data, S-3: IDH wildtype).
Results: The best performance was observed in the full cohort (S-1). In external validation, the combined-feature CR model achieved an AUC of 0.75, slightly, but significantly outperforming clinical-only (0.74) and imaging-only (0.68) models. DL models showed similar trends, though without statistical significance. In S-2 and S-3, combined models did not outperform clinical-only models. Exploratory analysis of CR models for overall survival prediction suggested greater relevance of imaging data: across all subsets, combined-feature models significantly outperformed clinical-only models, though with a modest advantage of 2-4 C-index points.
Conclusions: While confirming the predictive value of anatomical MRI sequences for glioblastoma prognosis, this multi-center study found standard CR and DL radiomics approaches offer minimal added value over demographic predictors such as age and gender. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_15548 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information Abler, D. Pusterla, O. Joye-Kühnis, A. Andratschke, N. Bach, M. Bink, A. Christ, S. M. Hagmann, P. Pouymayou, B. Pravatà, E. Radojewski, P. Reyes, M. Ruinelli, L. Schaer, R. Stieltjes, B. Treglia, G. Valenzuela, W. Wiest, R. Zoergiebel, S. Guckenberger, M. Tanadini-Lang, S. Depeursinge, A. Machine Learning Applications Background: Radiomics shows promise in characterizing glioblastoma, but its added value over clinical and molecular predictors has yet to be proven. This study assessed the added value of conventional radiomics (CR) and deep learning (DL) MRI radiomics for glioblastoma prognosis (<= 6 vs > 6 months survival) on a large multi-center dataset. Methods: After patient selection, our curated dataset gathers 1152 glioblastoma (WHO 2016) patients from five Swiss centers and one public source. It included clinical (age, gender), molecular (MGMT, IDH), and baseline MRI data (T1, T1 contrast, FLAIR, T2) with tumor regions. CR and DL models were developed using standard methods and evaluated on internal and external cohorts. Sub-analyses assessed models with different feature sets (imaging-only, clinical/molecular-only, combined-features) and patient subsets (S-1: all patients, S-2: with molecular data, S-3: IDH wildtype). Results: The best performance was observed in the full cohort (S-1). In external validation, the combined-feature CR model achieved an AUC of 0.75, slightly, but significantly outperforming clinical-only (0.74) and imaging-only (0.68) models. DL models showed similar trends, though without statistical significance. In S-2 and S-3, combined models did not outperform clinical-only models. Exploratory analysis of CR models for overall survival prediction suggested greater relevance of imaging data: across all subsets, combined-feature models significantly outperformed clinical-only models, though with a modest advantage of 2-4 C-index points. Conclusions: While confirming the predictive value of anatomical MRI sequences for glioblastoma prognosis, this multi-center study found standard CR and DL radiomics approaches offer minimal added value over demographic predictors such as age and gender. |
| title | The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2507.15548 |