Glioblastoma Overall Survival Prediction With Vision Transformers

TL;DR

This paper introduces a novel AI approach using Vision Transformers to predict glioblastoma patient survival from MRI images, simplifying the workflow and achieving competitive accuracy without tumor segmentation.

Contribution

The study demonstrates the application of Vision Transformers for glioblastoma survival prediction directly from MRI images, reducing computational complexity and eliminating the need for tumor segmentation.

Findings

Achieved 62.5% accuracy on BRATS dataset

Balanced performance across precision, recall, and F1 score

ViTs show promise for medical imaging tasks with limited data

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.