Clinical value of automated volumetric quantification of early ischemic tissue changes on non-contrast CT

Abstract

BackgroundQuantitative and automated volumetric evaluation of early ischemic changes on non-contrast CT (NCCT) has recently been proposed as a new tool to improve prognostic performance in patients undergoing endovascular therapy (EVT) for acute ischemic stroke (AIS). We aimed to test its clinical value compared with the Alberta Stroke Program Early CT Score (ASPECTS) in a large single-institutional patient cohort.MethodsA total of 1103 patients with AIS due to large vessel occlusion in the M1 or proximal M2 segments who underwent NCCT and EVT between January 2013 and November 2019 were retrospectively enrolled. Acute ischemic volumes (AIV) and ASPECTS were generated from the baseline NCCT through e-ASPECTS (Brainomix). Correlations were tested using Spearman’s coefficient. The predictive capabilities of AIV for a favorable outcome (modified Rankin Scale score at 90 days ≤2) were tested using multivariable logistic regression as well as machine-learning models. Performance of the models was assessed using receiver operating characteristic (ROC) curves and differences were tested using DeLong’s test.ResultsPatients with a favorable outcome had a significantly lower AIV (median 12.0 mL (IQR 5.7–21.7) vs 18.8 mL (IQR 9.4–33.9), p<0.001). AIV was highly correlated with ASPECTS (rho=0.78, p<0.001) and weakly correlated with the National Institutes of Health Stroke Scale score at baseline (rho=0.22, p<0.001), and was an independent predictor of an unfavorable clinical outcome (adjusted OR 0.97, 95% CI 0.96 to 0.98). No significant difference was found between machine-learning models using either AIV or ASPECTS or both metrics for predicting a good clinical outcome (p>0.05).ConclusionAIV is an independent predictor of clinical outcome and presented a non-inferior performance compared with ASPECTS, without clear advantages for prognostic modelling.

Potential and limitations of computed tomography images as predictors of the outcome of ischemic stroke events: a review

“…At the time of hospital admission, the consensus among researchers is that CT imaging contains relevant prognostic information. Unfortunately, all the studies that check if there is a statistically significant performance boost in adding such information conclude that these hybrid models are no better than their counterparts without imaging data (Brugnara et al, 2022;Ramos et al, 2022). This is consistent with the fact that imaging features are not regularly among the top most relevant features of the hybrid models (Brugnara et al, 2022;Ramos et al, 2022;Ozkara et al, 2023).…”

“…Of the five hybrid studies that used biomarkers, only one obtained them from experts annotations (Xie et al, 2019). Three were obtained in a semi-automatic (algorithmic labeling revised by humans) (Brugnara et al, 2020(Brugnara et al, , 2022Ozkara et al, 2023) and the other one in a fully automatic way (Jabal et al, 2022). • Using features extracted by algorithms (7/12 papers).…”

Deep Learning-Based Extraction of Biomarkers for the Prediction of the Functional Outcome of Ischemic Stroke Patients

“…In Brugnara et al [22] and Ramos et al [23], statistical tests were employed to compare tabular models with hybrid models. In Brugnara's work, they tested if adding the acute ischemic volumes and ASPECTS biomarkers would improve an otherwise tabular model.…”