Deep learning-based brain age predicts stroke recurrence in acute ischemic cerebrovascular disease

Abstract

Acute ischemic cerebrovascular disease (AICVD) exhibits high recurrence rates, necessitating novel biomarkers for refined risk stratification. While MRI-derived brain age correlates with stroke incidence, its prognostic utility for recurrence is unestablished. We developed the Mask-based Brain Age estimation Network (MBA Net), a deep learning framework designed for AICVD patients. MBA Net predicts contextual brain age (CBA) in non-infarcted regions by masking acute infarcts on T2-FLAIR images, thereby mitigating the confounding effects of dynamic infarcts during acute-phase neuroimaging. The model was trained on data from 5353 healthy individuals and then applied to a multicenter cohort of 10,890 AICVD patients. Brain age gap (BAG), defined as the deviation between CBA and chronological age, independently predicted stroke recurrence at both 3 months and 5 years, outperforming chronological age. Incorporating BAG into established prediction models significantly improved discriminative performance. These findings support brain age's potential utility in AI-driven precision strategies for secondary stroke prevention.

Fig. 1. Overview of the study design.

a Training procedure of the Mask-based Brain Age estimation Network (MBA Net). The model was developed to predict consistent brain age values for both masked and unmasked T2 fluid-attenuated inversion recovery (T2-FLAIR) images in heathy individuals. b Inference phase of contextual brain age (CBA) for patients with acute ischemic cerebrovascular disease (AICVD). The infarct lesion segmentation maps were converted into rectangular masks and subsequently applied to T2-FLAIR images to generate the corresponding masked T2-FLAIR images. These images were then processed through the MBA Net to estimate CBA and calculate the brain age gap (BAG). c Clinical application of the BAG in AICVD. For each additional year of BAG, the risks of stroke recurrence increased by 9% at 3 months and by 7% at 5 years.

Fig. 2. Scatterplots illustrating the relationships between contextual brain age (CBA), brain age gap (BAG), and chronologic age.

a Scatterplots of CBA per chronological age. Yellow and orange circles indicate male patients with positive and negative BAG, respectively, while blue and purple pentagrams represent female patients with positive and negative BAG, respectively. b Scatterplots of BAG per chronological age. Blue circles represent male patients, and red pentagrams represent female patients.

Fig. 3. Cumulative probability of recurrent stroke stratified by brain age gap (BAG) at 3 months and 5 years.

Time to outcome events were graphically presented using Kaplan–Meier curves and compared by the log-rank test. (a) and (b) show cumulative probability of recurrent stroke at 3 months and 5 years, respectively, based on binary BAG classification (positive BAG: red line; negative BAG: blue line). Adjusted covariates included age, sex, body mass index (BMI), medical history (ischemic stroke, coronary heart disease, hypertension, and diabetes mellitus), smoking history, pre-stroke modified Rankin Scale (mRS) score, National Institutes of Health Stroke Scale (NIHSS) score, index event, and Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification. (c) and (d) present cumulative probability at 3 months and 5 years, respectively, based on three-level BAG classification (accelerated aging: red line; normal aging: blue line; decelerated aging: green line). The multivariable model adjusted for age, sex, BMI, medical history (ischemic stroke, hypertension, and diabetes mellitus), smoking history, pre-stroke mRS score, NIHSS score, index event, TOAST classification and infarct volume.

Fig. 4. Examples of mismatch between brain age gap (BAG) and vascular risk factors.

a The patient’s contextual brain age (CBA) was “older” than chronological age. He had multiple vascular risk factors. b The patient’s CBA was “older” than chronological age. He did not have multiple vascular risk factors. c The patient’s CBA was “younger” than chronological age. He did not have multiple vascular risk factors. d The patient’s CBA was “younger” than chronological age. He had multiple vascular risk factors. The image on the left depicts an axial T2-fluid-attenuated inversion recovery scan, while the image on the right displays a diffusion-weighted imaging scan, with the red marker highlighting the infarct lesion.