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

Frontiers in Neuroscience

Yawu Liu¹  Tao Yuan²  Ranran Ban²  Guanmin Quan²  Shipeng Dai³  Weiwei Wang⁴  Jia-Liang Ren⁵ 

[1] Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland;Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China;Department of Radiology, Cangzhou City Hospital, Cangzhou, China;Department of Radiology, Handan Central Hospital, Handan, China;GE Healthcare China, Beijing, China;

关键词: acute ischemic stroke;    outcome;    magnetic resonance imaging;    apparent diffusion coefficient;    radiomics;   

DOI  :  10.3389/fnins.2021.730879

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown