# Glioblastoma Overall Survival Prediction With Vision Transformers

arXiv: https://arxiv.org/abs/2508.02439 · HTML: https://mlsanity.com/paper/2508.02439/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction](https://mlsanity.com/paper/2008.11576/) (2020)
- [Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture](https://mlsanity.com/paper/2101.01546/) (2021)
- [Glioblastoma Multiforme Patient Survival Prediction](https://mlsanity.com/paper/2101.10589/) (2021)
- [Survival and grade of the glioma prediction using transfer learning](https://mlsanity.com/paper/2402.03384/) (2024)

## Built on by (most cited first, 3 of 3)

- [Towards a Medical AI Scientist](https://mlsanity.com/paper/2603.28589/) (2026)
- [Radiomics- and Clinical Feature-Driven Prediction of Volumetric Response in Skull-Base Meningioma after CyberKnife Radiosurgery](https://mlsanity.com/paper/2604.24230/) (2026)
- [Touchless Intraoperative Image Access System Based on Vision-Based Hand Tracking](https://mlsanity.com/paper/2604.24235/) (2026)

## Abstract (arXiv)

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.