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Updated: Jun 27, 2026
Optimized System for Cerebral Perfusion Monitoring in the Rat Stroke Model of Intraluminal Middle Cerebral Artery Occlusion
Published on: February 17, 2013
Hypoperfusion Intensity Ratio as a Predictive Marker in Pre-Endovascular Treatment Perfusion Magnetic Resonance Image
Yoonkyung Lee1, Jae-Kwan Cha2,3, Dae-Hyun Kim1,4
1Department of Neurology, College of Medicine, Dong-A University, Busan, Republic of Korea.
Introduction:
Identifying 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.
Methods:
We 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.
Results:
A 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.
Conclusion:
HIR 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.
Insights
Hypoperfusion intensity ratio (HIR) on perfusion MRI can help identify intracranial atherosclerotic disease (ICAD) as a cause of acute middle cerebral artery (MCA) occlusion. This imaging marker aids in predicting ICAD before endovascular treatment.
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Identifying the cause of middle cerebral artery (MCA) occlusion is crucial for effective acute ischemic stroke treatment.
- Perfusion MRI provides quantitative imaging biomarkers, such as the hypoperfusion intensity ratio (HIR), to assess tissue viability.
Purpose of the Study:
- To investigate the association between HIR and intracranial atherosclerotic disease (ICAD) in patients with acute MCA occlusion.
- To develop and validate a machine learning model for predicting ICAD using clinical and imaging features, including HIR.
Main Methods:
- Retrospective analysis of 117 patients with acute MCA occlusion undergoing endovascular treatment and perfusion MRI.
- Classification of patients into ICAD and non-ICAD groups, followed by logistic regression and machine learning (XGBoost) analysis.
- Calculation of HIR cutoff using Youden's index and comparison of predictive performance with and without HIR.
Main Results:
- The ICAD group (34%) exhibited higher LDL-C, lower NIHSS, smaller DWI, and lower HIR.
- Late onset of symptoms (>6 hours), absence of spontaneous venousאין (SVS), absence of initial atrial fibrillation (AF), and low HIR (<0.31) were associated with ICAD.
- Inclusion of HIR significantly improved the predictive performance of the model (AUC 0.73 vs 0.88).
Conclusions:
- HIR is a valuable imaging marker for determining the mechanism of acute MCA occlusion, particularly for identifying ICAD.
- A machine learning model incorporating HIR, along with clinical and radiological factors, demonstrates high accuracy in predicting ICAD before endovascular treatment.
