Glioblastoma Overall Survival Prediction With Vision Transformers

• URL: http://arxiv.org/abs/2508.02439v2
• Date: Tue, 05 Aug 2025 07:03:24 GMT
• Title: Glioblastoma Overall Survival Prediction With Vision Transformers
• Authors: Yin Lin, Riccardo Barbieri, Domenico Aquino, Giuseppe Lauria, Marina Grisoli, Elena De Momi, Alberto Redaelli, Simona Ferrante,
• Abstract summary: Glioblastoma is one of the most aggressive and common brain tumors, with a median survival of 10-15 months.<n>In this study, we propose a novel Artificial Intelligence (AI) approach for Overall Survival (OS) prediction using Magnetic Resonance Imaging (MRI) images.<n>We exploit Vision Transformers (ViTs) to extract hidden features directly from MRI images, eliminating the need of tumor segmentation.<n>The proposed model was evaluated on the BRATS dataset, reaching an accuracy of 62.5% on the test set, comparable to the top-performing methods.
• Score: 6.318465743962574
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Glioblastoma is one of the most aggressive and common brain tumors, with a median survival of 10-15 months. Predicting Overall Survival (OS) is critical for personalizing treatment strategies and aligning clinical decisions with patient outcomes. In this study, we propose a novel Artificial Intelligence (AI) approach for OS prediction using Magnetic Resonance Imaging (MRI) images, exploiting Vision Transformers (ViTs) to extract hidden features directly from MRI images, eliminating the need of tumor segmentation. Unlike traditional approaches, our method simplifies the workflow and reduces computational resource requirements. The proposed model was evaluated on the BRATS dataset, reaching an accuracy of 62.5% on the test set, comparable to the top-performing methods. Additionally, it demonstrated balanced performance across precision, recall, and F1 score, overcoming the best model in these metrics. The dataset size limits the generalization of the ViT which typically requires larger datasets compared to convolutional neural networks. This limitation in generalization is observed across all the cited studies. This work highlights the applicability of ViTs for downsampled medical imaging tasks and establishes a foundation for OS prediction models that are computationally efficient and do not rely on segmentation.

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