Comparing Baseline and Day-1 Diffusion MRI Using Multimodal Deep Embeddings for Stroke Outcome Prediction

TL;DR

This study shows that 24-hour diffusion MRI combined with clinical data predicts stroke outcomes more accurately than baseline scans, highlighting the value of early post-treatment imaging for prognosis.

Contribution

It introduces a multimodal deep learning framework that integrates diffusion MRI, clinical variables, and lesion volume for improved stroke outcome prediction.

Findings

J1 MRI models outperform J0 models in predictive accuracy

Combining MRI with clinical and lesion-volume features enhances model robustness

Early post-treatment MRI provides better prognostic information than pre-treatment scans

Abstract

This study compares baseline (J0) and 24-hour (J1) diffusion magnetic resonance imaging (MRI) for predicting three-month functional outcomes after acute ischemic stroke (AIS). Seventy-four AIS patients with paired apparent diffusion coefficient (ADC) scans and clinical data were analyzed. Three-dimensional ResNet-50 embeddings were fused with structured clinical variables, reduced via principal component analysis (<=12 components), and classified using linear support vector machines with eight-fold stratified group cross-validation. J1 multimodal models achieved the highest predictive performance (AUC = 0.923 +/- 0.085), outperforming J0-based configurations (AUC <= 0.86). Incorporating lesion-volume features further improved model stability and interpretability. These findings demonstrate that early post-treatment diffusion MRI provides superior prognostic value to pre-treatment imaging and that combining MRI, clinical, and lesion-volume features produces a robust and interpretable framework for predicting three-month functional outcomes in AIS patients.