Clinical Research Collaboration for Stroke in Korea Imaging Repository: A Prospective Multicenter Neuroimaging Repository

Abstract

Abstract Background Prospective stroke registries have advanced our understanding of cerebrovascular disease, yet most reduce neuroimaging to categorical variables, forfeiting the multidimensional information inherent in clinical imaging. We describe the CRCS-K Imaging Repository, a prospective multicenter platform that systematically collects all stroke neuroimaging and integrates artificial intelligence (AI)-based automated quantification with clinical and outcome data through a dedicated research platform, AISCAN. Methods Building upon the Clinical Research Collaboration for Stroke in Korea (CRCS-K), a nationwide prospective registry, all neuroimaging (computed tomography [CT], magnetic resonance [MR], and angiography) performed during index hospitalization of consecutive acute ischemic stroke patients was collected from 18 comprehensive stroke centers. Imaging underwent centralized quality verification, sequence classification, and AI-based quantification. As a proof-of-concept application, we examined the association between pre-treatment imaging modality, treatment workflow efficiency, and functional outcomes in patients receiving intravenous thrombolysis (IVT) or endovascular treatment (EVT). Results From June 2022 through May 2025, 225,159 imaging sequences were collected from 20,792 patients. AI-based quantification modules converted these into standardized numeric features encompassing ischemic lesion volumes, perfusion parameters, white matter hyperintensity burden, and cerebral microbleed counts. Substantial inter-hospital variation in imaging modality selection was observed, with MR-first workflows ranging from 1.0% to 56.7% across centers. In the proof-of-concept analysis, each additional imaging sequence was associated with prolonged door-to-treatment times for both IVT and EVT. Propensity score overlap-weighted analyses suggested numerically more favorable functional outcomes with CT-based imaging among EVT-treated patients, whereas differences among IVT-treated patients were smaller and less consistent. Conclusions The CRCS-K Imaging Repository demonstrates the feasibility of large-scale, prospective neuroimaging collection integrated with AI-based quantification and clinical data. The infrastructure enables clinically consequential questions that conventional registries cannot address.

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