Evidence map›Paper›PMID 42348472›Full record

ArticleCerebrovascular diseases extra2026

Hypoperfusion Intensity Ratio as a Predictive Marker in Pre-Endovascular Treatment Perfusion Magnetic Resonance Image for Acute Middle Cerebral Artery Occlusion with Intracranial Atherosclerosis.

Yoonkyung Lee, Jae-Kwan Cha, Dae-Hyun Kim, Jung Hwa Seo

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Article in Cerebrovascular diseases extra, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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4 authors.

Yoonkyung LeeDepartment of Neurology, College of Medicine, Dong-A University, Busan, Republic of Korea.
Jae-Kwan ChaDepartment of Neurology, College of Medicine, Dong-A University, Busan, Republic of Korea, nrcjk65@gmail.com.
Dae-Hyun KimDepartment of Neurology, College of Medicine, Dong-A University, Busan, Republic of Korea.
Jung Hwa SeoDepartment of Neurology, College of Medicine, Dong-A University, Busan, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIdentifying the cause of middle cerebral artery (MCA) occlusion before endovascular treatment in acute ischemic stroke is useful. Hypoperfusion intensity ratio (HIR) is defined as the volumetric ratio of tissue with a time-to-maximum (Tmax) >10 s to that with Tmax >6 s in perfusion magnetic resonance image (MRI). In this study, using perfusion MRI, we hypothesized that HIR could be associated with intracranial atherosclerotic disease (ICAD) in acute MCA occlusion. We also performed machine learning-based feature importance analysis to identify factors associated with the mechanism of acute MCA occlusion.

methodsWe analyzed 117 patients with acute MCA occlusion treated with EVT and underwent RAPID MRI between March 2020 and December 2023. Patients were classified into the ICAD and non-ICAD groups. Clinical and imaging parameters were assessed using logistic regression. The cutoff value of HIR was calculated using Youden's index, and its predictive value was compared using the DeLong test. Development of a machine learning algorithm for predicting ICAD using XGBoost (eXtreme Gradient Boosting) and feature importance analysis were performed.

resultsA total of 34% were ICAD group with higher low-density lipoprotein cholesterol (LDL-C) levels, mild NIHSS, small DWI size, and low HIR. Late onset-to-puncture time (OTP) (>6 h) (OR: 9.67), no susceptibility vessel sign (SVS; OR: 9.05), no initial AF (OR: 63.67), low HIR (<0.31) (OR: 7.70) were associated with ICAD. Including HIR improved predictive performance (area under the curve [AUC] 0.73 vs. 0.88, p value <0.001). The XGBoost model showed high accuracy (0.92), SHAP value identified HIR, OTP, initial AF, SVS were important factors.

conclusionHIR is a potential marker to determine the mechanism of acute MCA occlusion. Combined with established clinical and radiological features, it helps in identifying the ICAD-related MCA occlusion before EVT.

Indexed as

Cerebrovascular CirculationEndovascular ProceduresInfarction, Middle Cerebral ArteryIntracranial ArteriosclerosisPerfusion Magnetic Resonance ImagingAgedFemaleHumansMaleMiddle AgedPredictive Learning ModelsPredictive Value of TestsRetrospective StudiesRisk AssessmentRisk FactorsTreatment OutcomeAcute ischemic strokeEndovascular proceduresIntracranial atherosclerosisPerfusion Magnetic resonance imagingThrombectomy

Identifiers

PMID42348472
PMCPMC13456670

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.