Integrating regional perfusion CT information to improve prediction of infarction after stroke

Abstract

Physiological evidence suggests that neighboring brain regions have similar perfusion characteristics (vascular supply, collateral blood flow). It is largely unknown whether integrating perfusion CT (pCT) information from the area surrounding a given voxel (i.e. the receptive field (RF)) improves the prediction of infarction of this voxel. Based on general linear regression models (GLMs) and using acute pCT-derived maps, we compared the added value of cuboid RF to predict the final infarct. To this aim, we included 144 stroke patients with acute pCT and follow-up MRI, used to delineate the final infarct. Overall, the performance of GLMs to predict the final infarct improved when using RF for all pCT maps (cerebral blood flow, cerebral blood volume, mean transit time and time-to-maximum of the tissue residual function (Tmax)). The highest performance was obtained with Tmax (glm(Tmax); AUC = 0.89 ± 0.03 with RF vs. 0.78 ± 0.02 without RF; p < 0.001) and with a model combining all perfusion parameters (glm(multi); AUC 0.89 ± 0.02 with RF vs. 0.79 ± 0.02 without RF; p < 0.001). These results suggest that prediction of infarction improves by integrating perfusion information from adjacent tissue. This approach may be applied in future studies to better identify ischemic core and penumbra thresholds and improve patient selection for acute stroke treatment.

Computed Tomography Perfusion–Based Prediction of Core Infarct and Tissue at Risk: Can Artificial Intelligence Help Reduce Radiation Exposure?

“…A few CTP-based AI studies have compared model outputs with RAPID, predominantly in terms of core infarct prediction but not from the perspective of patient triage for thrombectomy. 7,8 Our preliminary work, even though based on a smaller sample size, presents a proof of concept that an AI-based U-Net CNN model could provide comparable results to a clinically validated software. Given the reasonable SSIM of the model using only half of the CTP data, the feasibility of perfusion analysis using sparse CTP data needs to be further explored.…”

Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

“…Thirteen studies adopted conventional ML algorithms including k-nearest neighbor classification (24), general linear regression (47), random forest (13,15,25,34,36,38,41,48) and gradient boosting (11,26,36) classifiers. Twentyfive studies proposed DL-based approaches consisting of artificial neural network (ANN) (31) and various types of convolutional neural network (CNN) with some of the noteworthy popular architectures, including 2D and 3D U-Net (12,16,17,27,28,39,40,43,49,50), residual network (ResNet) (12,29,37,50), recurrent residual U-Net (R2U-Net) (52) and DeepMedic (32).…”

“…Eleven studies used CT perfusion source data and parametric maps as model input for core infarct estimation (12,13,29,31,33,42,44,47,50,53,54), including one study generating a synthesized pseudo-DWI map based on CTP parametric maps (42). Five studies used source images and features derived from non-contrast CT (41,52) and CT angiography (15,45,46).…”

“…In clinical practice, CT is more widely available for acute stroke triage, detecting large vessel occlusion, and selecting candidates for revascularization (8,55). Studies developing models training on CT perfusion data appeared late yet achieved comparable or better performance than MR-input models (12,29,31,33,42,44,47,50,52,53). One study employing a random forest classifier using features extracted from multi-phase CT angiography presented less satisfactory performance with a DSC score of 0.22 (15).…”

Intensive Care Admission and Management of Patients With Acute Ischemic Stroke: A Cross-sectional Survey of the European Society of Intensive Care Medicine

“…2 The overarching goal of AIS management in both stroke units and ICUs is to target therapeutic efforts to restore blood flow to the penumbra before irreversible tissue injury has occurred in order to minimize secondary brain injury and improve long-term functional outcomes and quality of life. 3,4 This is accomplished by conceptually optimizing brain perfusion and compensating for associated dysfunction in systemic organ systems. Because of the rapid and irreversible nature of ischemic brain injury, it is crucial for neurocritical care management to begin as early as possible in appropriate patients.…”