ABSTRACT
(1) Background: For acute ischemic strokes caused by large vessel occlusion, manually assessed thrombus volume and perviousness have been associated with treatment outcomes. However, the manual assessment of these characteristics is time-consuming and subject to inter-observer bias. Alternatively, a recently introduced fully automated deep learning-based algorithm can be used to consistently estimate full thrombus characteristics. Here, we exploratively assess the value of these novel biomarkers in terms of their association with stroke outcomes. (2) Methods: We studied two applications of automated full thrombus characterization as follows: one in a randomized trial, MR CLEAN-NO IV (n = 314), and another in a Dutch nationwide registry, MR CLEAN Registry (n = 1839). We used an automatic pipeline to determine the thrombus volume, perviousness, density, and heterogeneity. We assessed their relationship with the functional outcome defined as the modified Rankin Scale (mRS) at 90 days and two technical success measures as follows: successful final reperfusion, which is defined as an eTICI score of 2b-3, and successful first-pass reperfusion (FPS). (3) Results: Higher perviousness was significantly related to a better mRS in both MR CLEAN-NO IV and the MR CLEAN Registry. A lower thrombus volume and lower heterogeneity were only significantly related to better mRS scores in the MR CLEAN Registry. Only lower thrombus heterogeneity was significantly related to technical success; it was significantly related to a higher chance of FPS in the MR CLEAN-NO IV trial (OR = 0.55, 95% CI: 0.31-0.98) and successful reperfusion in the MR CLEAN Registry (OR = 0.88, 95% CI: 0.78-0.99). (4) Conclusions: Thrombus characteristics derived from automatic entire thrombus segmentations are significantly related to stroke outcomes.
ABSTRACT
BACKGROUND: Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic stroke might induce intracerebral hemorrhages which could negatively affect patient outcomes. Measuring white matter lesions size using deep learning (DL-WML) might help safely guide IVT administration. We aimed to develop, validate, and evaluate a DL-WML volume on CT compared to the Fazekas scale (WML-Faz) as a risk factor and IVT effect modifier in patients receiving EVT directly after IVT. METHODS: We developed a deep-learning model for WML segmentation on CT and validated with internal and external test sets. In a post hoc analysis of the MR CLEAN No-IV trial, we associated DL-WML volume and WML-Faz with symptomatic-intracerebral hemorrhage (sICH) and 90-day functional outcome according to the modified Rankin Scale (mRS). We used multiplicative interaction terms between WML measures and IVT administration to evaluate IVT treatment effect modification. Regression models were used to report unadjusted and adjusted common odds ratios (cOR/acOR). RESULTS: In total, 516 patients from the MR CLEAN No-IV trial (male/female, 291/225; age median, 71 [IQR, 62-79]) were analyzed. Both DL-WML volume and WML-Faz are associated with sICH (DL-WML volume acOR, 1.78 [95%CI, 1.17; 2.70]; WML-Faz acOR, 1.53 95%CI [1.02; 2.31]) and mRS (DL-WML volume acOR, 0.70 [95%CI, 0.55; 0.87], WML-Faz acOR, 0.73 [95%CI 0.60; 0.88]). Only in the unadjusted IVT effect modification analysis WML-Faz was associated with more sICH if IVT was given (p = 0.046). Neither WML measure was associated with worse mRS if IVT was given. CONCLUSION: DL-WML volume and WML-Faz had a similar relationship with functional outcome and sICH. Although more sICH might occur in patients with more severe WML-Faz receiving IVT, no worse functional outcome was observed. CLINICAL RELEVANCE STATEMENT: White matter lesion severity on baseline CT in acute ischemic stroke patients has a similar predictive value if measured with deep learning or the Fazekas scale. Safe administration of intravenous thrombolysis using white matter lesion severity should be further studied. KEY POINTS: White matter damage is a predisposing risk factor for intracranial hemorrhage in patients with acute ischemic stroke but remains difficult to measure on CT. White matter lesion volume on CT measured with deep learning had a similar association with symptomatic intracerebral hemorrhages and worse functional outcome as the Fazekas scale. A patient-level meta-analysis is required to study the benefit of white matter lesion severity-based selection for intravenous thrombolysis before endovascular treatment.
ABSTRACT
Thrombus imaging characteristics are associated with treatment success and functional outcomes in stroke patients. However, assessing these characteristics based on manual annotations is labor intensive and subject to observer bias. Therefore, we aimed to create an automated pipeline for consistent and fast full thrombus segmentation. We used multi-center, multi-scanner datasets of anterior circulation stroke patients with baseline NCCT and CTA for training (n = 228) and testing (n = 100). We first found the occlusion location using StrokeViewer LVO and created a bounding box around it. Subsequently, we trained dual modality U-Net based convolutional neural networks (CNNs) to segment the thrombus inside this bounding box. We experimented with: (1) U-Net with two input channels for NCCT and CTA, and U-Nets with two encoders where (2) concatenate, (3) add, and (4) weighted-sum operators were used for feature fusion. Furthermore, we proposed a dynamic bounding box algorithm to adjust the bounding box. The dynamic bounding box algorithm reduces the missed cases but does not improve Dice. The two-encoder U-Net with a weighted-sum feature fusion shows the best performance (surface Dice 0.78, Dice 0.62, and 4% missed cases). Final segmentation results have high spatial accuracies and can therefore be used to determine thrombus characteristics and potentially benefit radiologists in clinical practice.
