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<p>Nonlacunar covert brain infarcts (CBIs) on noncontrast CT (NCCT) are frequently overlooked yet may signal high-risk etiologies such as large-artery atherosclerosis (LAA) or cardioembolism (CE). We investigated the clinical-etiological significance of nonlacunar CBIs and developed a deep learning model for their automated detection. Among 628 patients with first-ever symptomatic ischemic stroke from a multicenter registry, nonlacunar CBIs were identified in 13.5% and were associated with atrial fibrillation (adjusted OR 2.03; p=0.002) and with stepwise worsening large-artery disease, manifested as either severe stenosis (adjusted OR 2.48, p=0.015) or arterial occlusion (adjusted OR 2.99, p=0.004). Etiological concordance was substantial and statistically significant. Among CBIs with relevant stenosis, LAA was the predominant subsequent etiology (48.5%, p=0.019), and among those without relevant stenosis, cardioembolism predominated (46.2%, p=0.011). Patients with nonlacunar CBIs had worse 3-month functional independence (51.8% vs 73.1%; adjusted p=0.002) and impaired recovery trajectories (p&lt;0.001). A deep learning model, developed on a separate cohort (n=758) and validated across three independent external cohorts (n=1,680), achieved sensitivities of 0.722–0.755 and specificities of 0.797–0.932. These findings reframe nonlacunar CBIs as sentinel markers of persistent high-risk pathology, and automated detection on routine NCCT may help identify patients who warrant etiologic evaluation and targeted secondary prevention before a subsequent disabling stroke of the same underlying etiology occurs.</p>

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Nonlacunar covert brain infarcts on noncontrast CT as sentinel imaging markers of stroke etiology and outcome

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Seunghan Yeom,
  • Wi-Sun Ryu,
  • Myungjae Lee,
  • Dongmin Kim,
  • Gihun Park,
  • Leonard Sunwoo,
  • Chi Kyung Kim,
  • Beom Joon Kim

摘要

Nonlacunar covert brain infarcts (CBIs) on noncontrast CT (NCCT) are frequently overlooked yet may signal high-risk etiologies such as large-artery atherosclerosis (LAA) or cardioembolism (CE). We investigated the clinical-etiological significance of nonlacunar CBIs and developed a deep learning model for their automated detection. Among 628 patients with first-ever symptomatic ischemic stroke from a multicenter registry, nonlacunar CBIs were identified in 13.5% and were associated with atrial fibrillation (adjusted OR 2.03; p=0.002) and with stepwise worsening large-artery disease, manifested as either severe stenosis (adjusted OR 2.48, p=0.015) or arterial occlusion (adjusted OR 2.99, p=0.004). Etiological concordance was substantial and statistically significant. Among CBIs with relevant stenosis, LAA was the predominant subsequent etiology (48.5%, p=0.019), and among those without relevant stenosis, cardioembolism predominated (46.2%, p=0.011). Patients with nonlacunar CBIs had worse 3-month functional independence (51.8% vs 73.1%; adjusted p=0.002) and impaired recovery trajectories (p<0.001). A deep learning model, developed on a separate cohort (n=758) and validated across three independent external cohorts (n=1,680), achieved sensitivities of 0.722–0.755 and specificities of 0.797–0.932. These findings reframe nonlacunar CBIs as sentinel markers of persistent high-risk pathology, and automated detection on routine NCCT may help identify patients who warrant etiologic evaluation and targeted secondary prevention before a subsequent disabling stroke of the same underlying etiology occurs.