FLAIR and ADC Image-Based Radiomics Features as Predictive Biomarkers of Unfavorable Outcome in Patients With Acute Ischemic Stroke

Abstract

At present, it is still challenging to predict the clinical outcome of acute ischemic stroke (AIS). In this retrospective study, we explored whether radiomics features extracted from fluid-attenuated inversion recovery (FLAIR) and apparent diffusion coefficient (ADC) images can predict clinical outcome of patients with AIS. Patients with AIS were divided into a training (n = 110) and an external validation (n = 80) sets. A total of 753 radiomics features were extracted from each FLAIR and ADC image of the 190 patients. Interquartile range (IQR), Wilcoxon rank sum test, and least absolute shrinkage and selection operator (LASSO) were used to reduce the feature dimension. The six strongest radiomics features were related to an unfavorable outcome of AIS. A logistic regression analysis was employed for selection of potential predominating clinical and conventional magnetic resonance imaging (MRI) factors. Subsequently, we developed several models based on clinical and conventional MRI factors and radiomics features to predict the outcome of AIS patients. For predicting unfavorable outcome [modified Rankin scale (mRS) > 2] in the training set, the area under the receiver operating characteristic curve (AUC) of ADC radiomics model was 0.772, FLAIR radiomics model 0.731, ADC and FLAIR radiomics model 0.815, clinical model 0.791, and clinical and conventional MRI model 0.782. In the external validation set, the AUCs for the prediction with ADC radiomics model was 0.792, FLAIR radiomics model 0.707, ADC and FLAIR radiomics model 0.825, clinical model 0.763, and clinical and conventional MRI model 0.751. When adding radiomics features to the combined model, the AUCs for predicting unfavorable outcome in the training and external validation sets were 0.926 and 0.864, respectively. Our results indicate that the radiomics features extracted from FLAIR and ADC can be instrumental biomarkers to predict unfavorable clinical outcome of AIS and would additionally improve predictive performance when adding to combined model.

Prognosis of ischemic stroke predicted by machine learning based on multi-modal MRI radiomics

“…Zhang et al ( 5 ) developed the machine learning model-based diffusion weighed imaging (DWI)/ADC radiomic features to classify ischemic stroke onset time. Quan et al ( 8 ) constructed the unfavorable outcome model based on the radiomic feature extracted from FLAIR and ADC image. Moreover, susceptibility weighted imaging (SWI), reflecting the oxygen extraction fraction of brain tissues, has been demonstrated as a useful predictor of early infarct size and early-stage clinical prognosis in acute ischemic stroke ( 9 ).…”

Clinical features and FLAIR radiomics nomogram for predicting functional outcomes after thrombolysis in ischaemic stroke

“…Radiomics is a novel developed data analysis technique that can transform medical images into high-throughput quantitative features, assess the heterogeneity of diseased tissue, and reflect the physiological and pathological status and has been applied to the prediction of clinical outcomes. At present, radiomics has a promising application prospect in stroke, including the diagnosis of stroke (Peter et al, 2017),early prediction of clinical outcome (Wen et al, 2020) and evaluation of medium and long term prognosis (Tang et al, 2020;Quan et al, 2021;Wang et al, 2021;Zhou et al, 2022). Wen et al (2020) developed a model based on radiological features extracted from computed tomography non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) to predict the development of malignant acute middle cerebral Artery Infarction (mMCAi) in patients with cerebral infarction.…”

“…Wen et al (2020) developed a model based on radiological features extracted from computed tomography non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) to predict the development of malignant acute middle cerebral Artery Infarction (mMCAi) in patients with cerebral infarction. Several recent studies have shown that the clinical-radiomics model extracted from diffusion-weighted imaging (DWI), fluid attenuated inversion recovery (FLAIR) or apparent diffusion coefficient (ADC) achieved satisfactory performance in predicting AIS outcomes (Tang et al, 2020;Quan et al, 2021;Wang et al, 2021;Zhou et al, 2022). Most patients with ischaemic stroke receive only routine sequences, including DWI, ADC and FLAIR.…”

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

“…Radiomics can facilitate better clinical decision by improving the process of detecting heterogeneous findings without visible abnormalities in medical images through high-throughput quantitative analysis of statistical features (Gillies et al, 2016 ). Successful applications of radiomics in acute stroke have been reported in prediction of the hematoma expansion (Ma et al, 2019 ; Xie et al, 2020 ; Liu et al, 2021 ; Song et al, 2021 ), successful recanalization (Qiu et al, 2019 ; Hofmeister et al, 2020 ), recurrence (Tang et al, 2022 ) and functional outcome (Haider et al, 2021 ; Quan et al, 2021 ; Wang et al, 2021 ). The discrimination of hematomas etiologies (Zhang et al, 2019 ; Nawabi et al, 2020 ) using radiomics analysis had been reported as well.…”