Next Article in Journal
Prognostic Impact of Active Mechanical Circulatory Support in Cardiogenic Shock Complicating Acute Myocardial Infarction, Results from the Culprit-Shock Trial
Next Article in Special Issue
Temperature-Induced Changes in Reperfused Stroke: Inflammatory and Thrombolytic Biomarkers
Previous Article in Journal
To Correct or Not Correct? Actual Evidence, Controversy and the Questions That Remain Open
Previous Article in Special Issue
Dynamic Hyperglycemic Patterns Predict Adverse Outcomes in Patients with Acute Ischemic Stroke Undergoing Mechanical Thrombectomy
Open AccessArticle

Novel Estimation of Penumbra Zone Based on Infarct Growth Using Machine Learning Techniques in Acute Ischemic Stroke

1
Clinical Research Institute, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul 06351, Korea
2
Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul 06351, Korea
3
Department of Radiology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul 06351, Korea
4
Department of Neurology, Yonsei University, Seoul 03722, Korea
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(6), 1977; https://doi.org/10.3390/jcm9061977
Received: 19 May 2020 / Revised: 15 June 2020 / Accepted: 22 June 2020 / Published: 24 June 2020
(This article belongs to the Special Issue Thrombolysis and Thrombectomy in Acute Ischemic Stroke)
While the penumbra zone is traditionally assessed based on perfusion–diffusion mismatch, it can be assessed based on machine learning (ML) prediction of infarct growth. The purpose of this work was to develop and validate an ML method for the prediction of infarct growth distribution and volume, in cases of successful (SR) and unsuccessful recanalization (UR). Pre-treatment perfusion-weighted, diffusion-weighted imaging (DWI) data, and final infarct lesions annotated from day-7 DWI from patients with middle cerebral artery occlusion were utilized to develop and validate two ML models for prediction of tissue fate. SR and UR models were developed from data in patients with modified treatment in cerebral infarction (mTICI) scores of 2b–3 and 0–2a, respectively. When compared to manual infarct annotation, ML-based infarct volume predictions resulted in an intraclass correlation coefficient (ICC) of 0.73 (95% CI = 0.31–0.91, p < 0.01) for UR, and an ICC of 0.87 (95% CI = 0.73–0.94, p < 0.001) for SR. Favorable outcomes for mismatch presence and absence in SR were 50% and 36%, respectively, while they were 61%, 56%, and 25%, respectively, for the low, intermediate, and high infarct growth groups. The presented method can offer novel and alternative insights into selecting patients for recanalization therapy and predicting functional outcome. View Full-Text
Keywords: stroke; ischemia; machine learning; cerebral infarction stroke; ischemia; machine learning; cerebral infarction
Show Figures

Figure 1

MDPI and ACS Style

Kim, Y.-C.; Kim, H.J.; Chung, J.-W.; Kim, I.G.; Seong, M.J.; Kim, K.H.; Jeon, P.; Nam, H.S.; Seo, W.-K.; Kim, G.-M.; Bang, O.Y. Novel Estimation of Penumbra Zone Based on Infarct Growth Using Machine Learning Techniques in Acute Ischemic Stroke. J. Clin. Med. 2020, 9, 1977.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop