IMG-66. Self-supervised multimodal learning for survival prediction in glioblastoma: a multicenter study from the ReSPOND consortium

PURPOSE Glioblastoma is the most aggressive adult brain tumor, with a median overall survival of approximately 15 months. It is important to build accurate prognostic models for glioblastoma patients to inform clinical management and trials. This study proposes a self-supervised learning-based approach with multimodal data integration for survival prediction and prognostic stratification of glioblastoma patients on the ReSPOND consortium. METHODS We curated a multi-parametric MRI dataset (T1, T1CE, T2, FLAIR) of 3,119 glioblastoma patients from 22 institutions across 3 continents. Masked autoencoder (MAE) was adapted to pretrain a Vision Transformer (ViT) encoder by reconstructing the masked image patches. The encoder was utilized for extracting patch embeddings for survival tasks, with cross-attention mechanism to incorporate the molecular and clinical information (age, sex, extent of resection, MGMT) to guide imaging feature aggregation. Imaging and clinical embeddings were fused through a multi-layer perceptron (MLP) for log-risk hazard estimation, optimized using Cox partial likelihood. Model performance and generalizability were assessed via k-fold cross-validation on the ReSPOND consortium and the leave-one-site-out validation was performed on 11 institutions comparing with CoxPH, DeepSurv and DeepHit. Prognostic risk stratification via Kaplan-Meier analysis divided the patients into low-, medium- and high-risk subgroups per site. RESULTS Multimodal data integration using the proposed framework achieved the highest C-index (0.674 ± 0.017) on the ReSPOND consortium. Integration of clinical information and MGMT consistently boosted the performance of the proposed model across sites (0.615 ± 0.046 vs. 0.662 ± 0.044). The imaging-based approaches, i.e., radiomics and convolutional neural network (CNN) features performed less robustly. The Kaplan-Meier curves and log-rank tests suggested the proposed framework achieved more separable prognostic subgroups. CONCLUSION The proposed self-supervised multimodal learning framework shows promise for survival prediction and prognostic risk stratification in glioblastoma. It highlights the challenge for clinical model deployment due to the data heterogeneity in multi-institutional cohort.