IMG-100. Cross-Institutional Validation of a Multimodal Deep Learning Model for Glioblastoma Survival Prediction

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Abstract Glioblastoma (GBM) is an aggressive tumor with a ~15-month median survival and high interpatient variability. Deep learning (DL) models using structural MRI have shown promise for individualized survival prediction, but most are developed and tested at a single institution, limiting generalizability. We externally validated a multimodal DL model trained on heterogeneous data from three sites using an independent GBM cohort from Moffitt. This study offers a rare and rigorous assessment of model performance on truly unseen data, highlighting its potential for real-world clinical deployment. We identified 149 patients with confirmed glioblastoma at Moffitt who had preoperative MRI scans acquired within four weeks of surgery. Each patient had four axial MRI sequences available: T1, T2, T1-postcontrast, and T2-FLAIR. The pretrained multimodal DL model was applied without additional fine-tuning. The model uses a ViT architecture and incorporates age as a clinical variable via a late fusion approach. Patients were stratified into high- and low-risk groups based on predicted survival scores. The concordance index (C-Index) and time-dependent area under the receiver operator curve (AUC) at one year were calculated (±95% CI). The model achieved a C-index of 0.680 ± 0.051 and an AUC of 0.773 ± 0.079. Kaplan–Meier analysis showed a significant difference in overall survival between model-predicted high- and low-risk groups (hazard ratio = 1.69, 95% CI: 1.22–2.36; p = 0.001). Age was significantly higher in the high-risk group (p < 0.05), consistent with its known prognostic relevance. Incorporating age via late fusion modestly improved performance, increasing the C-index to 0.686 ± 0.052. This externally validated multimodal deep learning model accurately stratified glioblastoma patients by survival risk using routine preoperative MRIs. The findings highlight the feasibility of deploying such models across institutions and support their potential to enable personalized prognosis and risk-adapted treatment planning in clinical neuro-oncology.

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