  Background: This research aims to improve glioblastoma survival prediction by
integrating MR images, clinical and molecular-pathologic data in a
transformer-based deep learning model, addressing data heterogeneity and
performance generalizability. Method: We propose and evaluate a
transformer-based non-linear and non-proportional survival prediction model.
The model employs self-supervised learning techniques to effectively encode the
high-dimensional MRI input for integration with non-imaging data using
cross-attention. To demonstrate model generalizability, the model is assessed
with the time-dependent concordance index (Cdt) in two training setups using
three independent public test sets: UPenn-GBM, UCSF-PDGM, and RHUH-GBM, each
comprising 378, 366, and 36 cases, respectively. Results: The proposed
transformer model achieved promising performance for imaging as well as
non-imaging data, effectively integrating both modalities for enhanced
performance (UPenn-GBM test-set, imaging Cdt 0.645, multimodal Cdt 0.707) while
outperforming state-of-the-art late-fusion 3D-CNN-based models. Consistent
performance was observed across the three independent multicenter test sets
with Cdt values of 0.707 (UPenn-GBM, internal test set), 0.672 (UCSF-PDGM,
first external test set) and 0.618 (RHUH-GBM, second external test set). The
model achieved significant discrimination between patients with favorable and
unfavorable survival for all three datasets (logrank p 1.9\times{10}^{-8},
9.7\times{10}^{-3}, and 1.2\times{10}^{-2}). Conclusions: The proposed
transformer-based survival prediction model integrates complementary
information from diverse input modalities, contributing to improved
glioblastoma survival prediction compared to state-of-the-art methods.
Consistent performance was observed across institutions supporting model
generalizability.
