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

Abstract

ObjectiveIncreased risk of stroke is highly associated with psychiatric disorders. We aimed to conduct the machine learning model based on multi-modal magnetic resonance imaging (MRI) radiomics predicting the prognosis of ischemic stroke.MethodsThis study retrospectively analyzed 148 patients with acute ischemic stroke due to anterior circulation artery occlusion. Based on the modified Rankin Scale (mRS) score, patients were divided into good (mRS ≤ 2) and poor (mRS > 2) outcome groups. Segmentation of the infarct region was performed by manually outlining a mask of the lesion on diffusion-weighted images (DWI) using MRIcron software. The apparent diffusion coefficient (ADC), fluid decay inversion recoverage (FLAIR), susceptibility weighted imaging (SWI) and T1-weighted (T1w) images were aligned to the DWI images and the radiomic features within the lesion area were extracted for each image modality. The calculations were done using pyradiomics software and a total of 4,744 stroke-related imaging features were automatically calculated. Next, feature selection based on recursive feature elimination was used for each modality and three radiomic features were extracted from each modality plus one feature from the lesion mask, for a total of 16 radiomic features. At last, five machine learning (ML) models were trained and tested to predict stroke prognosis, calculate the received operating characteristic (ROC) curves and other parameters, evaluate the performance of the models and validate their predictive efficacy by five-fold cross-validation.ResultsSixteen radiomic features were selected to construct the ML models for prognostic classification. By five-fold cross-validation, light gradient boosting machine (LightGBM) model-based muti-modal MRI radiomic features performed best in binary prognostic classification with accuracy of 0.831, sensitivity of 0.739, specificity of 0.902, F1-score of 0.788 and an area under the curve (AUC) of 0.902.ConclusionThe ML models based on muti-modal MRI radiomics are of high value for predicting clinical outcomes in acute stroke patients.

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

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Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review

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