Abstract
BackgroundRecent trials have shown promise in intra-arterial thrombectomy after the first 6–24 h of stroke onset. Quick and precise identification of the salvageable tissue is essential for successful stroke management. In this study, we examined the feasibility of machine learning (ML) approaches for differentiating the ischemic penumbra (IP) from the infarct core (IC) by using diffusion tensor imaging (DTI)-derived metrics.MethodsFourteen male rats subjected to permanent middle cerebral artery occlusion (pMCAO) were included in this study. Using a 7 T magnetic resonance imaging, DTI metrics such as fractional anisotropy, pure anisotropy, diffusion magnitude, mean diffusivity (MD), axial diffusivity, and radial diffusivity were derived. The MD and relative cerebral blood flow maps were coregistered to define the IP and IC at 0.5 h after pMCAO. A 2-level classifier was proposed based on DTI-derived metrics to classify stroke hemispheres into the IP, IC, and normal tissue (NT). The classification performance was evaluated using leave-one-out cross validation.ResultsThe IC and non-IC can be accurately segmented by the proposed 2-level classifier with an area under the receiver operating characteristic curve (AUC) between 0.99 and 1.00, and with accuracies between 96.3 and 96.7%. For the training dataset, the non-IC can be further classified into the IP and NT with an AUC between 0.96 and 0.98, and with accuracies between 95.0 and 95.9%. For the testing dataset, the classification accuracy for IC and non-IC was 96.0 ± 2.3% whereas for IP and NT, it was 80.1 ± 8.0%. Overall, we achieved the accuracy of 88.1 ± 6.7% for classifying three tissue subtypes (IP, IC, and NT) in the stroke hemisphere and the estimated lesion volumes were not significantly different from those of the ground truth (p = .56, .94, and .78, respectively).ConclusionsOur method achieved comparable results to the conventional approach using perfusion–diffusion mismatch. We suggest that a single DTI sequence along with ML algorithms is capable of dichotomizing ischemic tissue into the IC and IP.
Highlights
Recent trials have shown promise in intra-arterial thrombectomy after the first 6–24 h of stroke onset
An infarct core (IC) can be identified through diffusion-weighted imaging (DWI) and combined with the hypoperfusion area depicted by perfusion-weighted imaging (PWI), which allows for the specific definition of the salvageable ischemic penumbra (IP) and IC by using the concept of perfusion–diffusion mismatch (PDM)
Two-level classification We propose a 2-level classification model composed of two binary classifiers to hierarchically classify the stroke hemisphere into three tissue subtypes, as displayed in Fig. 2a, and the classification performance was evaluated through the leave-one-out cross validation (LOOCV) method (i.e., 13 rats were used for training and a remaining rat for testing each time, Fig. 2b)
Summary
Recent trials have shown promise in intra-arterial thrombectomy after the first 6–24 h of stroke onset. Quick and precise identification of the salvageable tissue is essential for successful stroke management. Treatment options for acute ischemic stroke (AIS) are rapid recanalization of the occluded large vessels by using intravenous (IV) thrombolysis with tissue plasminogen activator (tPA) and intra-arterial (IA) thrombectomy to mechanically disrupt or remove the thrombus. In the DAWN and DEFUSE 3 trials, which included acute stroke patients within 6–24 h of onset, obtaining perfusion imaging computed tomography (CT) perfusion or magnetic resonance imaging (MRI) perfusion-weighted imaging (PWI) or an MRI with a diffusion-weighted imaging (DWI) sequence was recommended to help determine whether the patient is a candidate for mechanical thrombectomy [2, 5]. Quick and accurate delineation of IP is demanded by clinicians for AIS management
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