Automated ischemic stroke lesion detection on non-contrast brain CT: a large-scale clinical feasibility test AI stroke lesion detection on NCCT - Heo, JoonNyung; Ryu, Wi-Sun; Chung, Jong-Won; Kim, Chi Kyung; Kim, Joon-Tae; Lee, Myungjae; Kim, Dongmin; Sunwoo, Leonard; Ospel, Johanna M.; Singh, Nishita; Bae, Hee-Joon; Kim, Beom Joon (Frontiers in Neuroscience, 2025)
BackgroundNon-contrast CT (NCCT) is widely used imaging modality for acute stroke imaging but often fails to detect subtle early ischemic changes. Such underestimation can lead clinicians to overlook tissue-level information. This study aimed to develop and externally validate automated software for detecting ischemic lesions on NCCT and to assess its clinical feasibility in stroke patients undergoing endovascular thrombectomy.MethodsIn this retrospective, multicenter cohort study (May 2011–April 2024), a modified 3D U-Net model was trained using paired NCCT and diffusion-weighted imaging (DWI) data from 2,214 patients with acute ischemic stroke. External validation was performed in 458 subjects. Clinical feasibility was assessed in 603 endovascular thrombectomy-treated patients with complete recanalization. Model outputs were compared against expert-annotated DWI lesions for sensitivity, specificity, and volumetric correlation. Clinical endpoints included follow-up DWI lesion volumes, hemorrhagic transformation, and 3-month modified Rankin Scale outcomes.ResultsA total of 458 subjects were evaluated for external validation (mean age, 64 years ± 16; 265 men). The model achieved 75.3% sensitivity (95% CI, 70.9–79.9%) and 79.1% specificity (95% CI, 77.1–81.3%). In the feasibility cohort (n = 603; mean age, 69 years ± 13; 362 men), NCCT-derived lesion volumes correlated with follow-up DWI volumes (ρ = 0.60, p < 0.001). Lesions >50 mL were associated with reduced favorable outcomes (17.3% [26/150] vs. 54.2% [246/453], p < 0.001) and higher hemorrhagic transformation rates (66.0% [99/150] vs. 46.3% [210/453], p < 0.001). Radiomics features improved hemorrhagic transformation prediction beyond clinical variables alone (area under the receiver operating characteristic curve, 0.833 vs. 0.626; p = 0.003).ConclusionThe automated NCCT-based lesion detection model demonstrated reliable diagnostic performance and provided clinically relevant prognostic information in endovascular thrombectomy-treated stroke patients.