Lightweight ensemble vision transformer framework for non-invasive survival prediction in glioblastoma

Glioblastoma is the most aggressive primary brain tumor, characterized by rapid progression and poor prognosis. Predicting overall survival (OS) at diagnosis can support clinicians in tailoring treatments to patient-specific risk levels. Traditional approaches often rely on tumor segmentation or pre-trained models based on natural images, both of which present limitations in scalability and clinical practicality. In this work, we introduce GLiT-Net, a lightweight ensemble framework based on Vision Transformers (ViTs), designed for OS classification using only MRI scans and patient age, without the need for segmentation or external pre-training. To address data scarcity, GLiT-Net operates on reduced-dimensional MRI volumes and leverages targeted data augmentation strategies to enhance generalizability. We evaluate the model on the BraTS 2020 dataset, selecting 118 glioblastoma patients with gross total resection. Survival times are discretized into three balanced classes using percentile thresholds. Our ensemble framework integrates predictions from ten compact ViT models, trained under a repeated nested cross-validation scheme. GLiT-Net achieves an average test accuracy of 70.3% and an F1-score of 68.7%, outperforming existing approaches in the literature which report accuracies up to 65%. The model maintains predictive strength despite reduced image resolution and limited data availability. These findings underscore the feasibility of segmentation-free, non-invasive survival prediction using simplified ViT-based architectures. While external validation and integration of additional clinical or molecular features remain future directions, GLiT-Net offers a practical and effective solution for automated risk stratification in glioblastoma care. • Accurate prediction of overall survival in glioblastoma is essential for treatment planning, yet current approaches rely heavily on tumor segmentation to achieve reliable performance. • A compact ensemble of vision transformers predicts survival directly from reduced volumetric MRI without requiring tumor segmentation and remains stable across varying contrast conditions, matching the reliability of more complex state-of-the-art pipelines. • This framework delivers immediate prognostic insight following MRI acquisition, enabling faster, contrast-invariant, segmentation-free support for personalized treatment planning and more efficient patient management.