Texture analysis of apparent diffusion coefficient maps in predicting the clinical functional outcomes of acute ischemic stroke

Abstract

PurposeTo investigate texture analysis (TA) based on apparent diffusion coefficient (ADC) map in predicting acute ischemic stroke (AIS) prognosis and discriminating TA features in stroke subtypes.MethodsThis retrospective study included patients with AIS between January 2018 and April 2021. The patients were assigned to the favorable [modified Rankin Scale (mRS) score ≤ 2] and unfavorable (mRS score > 2) outcome groups. All patients underwent stroke subtyping according to the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) classification. The TA features were extracted from infarction lesions on the ADC map. The demographic characteristics, clinical characteristics, and texture features were used to construct prediction models with recurrent neural network (RNN). The receiver operating characteristic (ROC) curves were implemented to evaluate the performance of the predictive models.ResultsA total of 1,003 patients (682 male; mean age 65.90 ± 12.44) with AIS having documented the 90-day mRS score were identified, including 840 with favorable outcomes. In the validation set, the area under the curve (AUC) of the predictive model using only clinical characteristics achieved an AUC of 0.56, texture model 0.77, the model combining both clinical and texture features showed better with an AUC of 0.78. The texture feature profiles differed between large artery atherosclerosis (LAA) and small artery occlusion (SAO) subtypes (all p < 0.05). The AUC of combined prediction models for LAA and SAO subtypes was 0.80 and 0.81.ConclusionTexture analysis based on ADC map could be useful as an adjunctive tool for predicting ischemic stroke prognosis.

Research on prognostic risk assessment model for acute ischemic stroke based on imaging and multidimensional data

“…Wang et al ( 9 ) and Zhou et al ( 8 ) used a multivariate logistic regression model to construct an imaging omics nomogram containing patient characteristics and imaging omics characteristics, and the AUC used to predict stroke outcome was greater than 0.80. Sun et al ( 10 ) used clinical features and apparent diffusion coefficient maps to predict poor prognosis of acute stroke (mRS score >2) and the AUC was 0.80. These models were superior to models using non-imaging data, and the clinical data were continuous and related, which demonstrates the great potential of the combination.…”

“…Computer-aided diagnosis (CAD) based on MRI has received extensive attention from researchers both domestically and internationally. For example, the texture analysis of apparent diffusion coefficient maps and diffusion-weighted imaging were used to predict the prognosis and subtype of ischemic stroke (8)(9)(10). A systematic review also demonstrated that a combined model combining clinical and imaging variables was more predictive of stroke outcome (11).…”