A CT-based radiomics nomogram for classification of intraparenchymal hyperdense areas in patients with acute ischemic stroke following mechanical thrombectomy treatment

Abstract

ObjectivesTo develop and validate a radiomic-based model for differentiating hemorrhage from iodinated contrast extravasation of intraparenchymal hyperdense areas (HDA) following mechanical thrombectomy treatment in acute ischemic stroke.MethodsA total of 100 and four patients with intraparenchymal HDA on initial post-operative CT were included in this study. The patients who met criteria were divided into a primary and a validation cohort. A training cohort was constructed using Synthetic Minority Oversampling Technique on the primary cohort to achieve group balance. Thereafter, a radiomics score was calculated and the radiomic model was constructed. Clinical factors were assessed to build clinical model. Combined with the Rad-score and independent clinical factors, a combined model was constructed. Different models were assessed using the area under the receiver operator characteristic curves. The combined model was visualized as nomogram, and assessed with calibration and clinical usefulness.ResultsCardiogenic diseases, intraoperative tirofiban administration and preoperative national institute of health stroke scale were selected as independent predictors to construct the clinical model with area under curve (AUC) of 0.756 and 0.693 in the training and validation cohort, respectively. Our data demonstrated that the radiomic model showed good discrimination in the training (AUC, 0.955) and validation cohort (AUC, 0.869). The combined nomogram model showed optimal discrimination in the training (AUC, 0.972) and validation cohort (AUC, 0.926). Decision curve analysis demonstrated the combined model had a higher overall net benefit in differentiating hemorrhage from iodinated contrast extravasation in terms of clinical usefulness.ConclusionsThe nomogram shows favorable efficacy for differentiating hemorrhage from iodinated contrast extravasation, which might provide an individualized tool for precision therapy.

A CT texture-based nomogram for predicting futile reperfusion in patients with intraparenchymal hyperdensity after endovascular thrombectomy for acute anterior circulation large vessel occlusion

“…Frontiers in Neurology 09 frontiersin.org aspects of stroke, including the diagnosis of stroke lesions (18-20) and cerebral hemorrhage (21,22). In the future, we should intensify our research on CT texture analysis to fully unveil its value and expand its application.…”

Application of radiomics in ischemic stroke

“…The differentiation between contrast agent extravasation and post-thrombectomy hemorrhage is often difficult using imaging features; this challenge was addressed in studies by Ma et al. 46 and Chen et al. 47 By integrating radiomic features and incorporating multiple independent predictive factors, these researchers constructed models that were proficient at distinguishing between hemorrhage and contrast agent extravasation.…”

Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks

“…The [30]. Figure 1 depicts the process of obtaining datasets, including the application of exclusion criteria, as well as the stages of training, validation, and tests.…”