Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke

Abstract

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes.

Materials and methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses.

Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness.

Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

Keywords: Diffusion-weighted imaging; Ischemic stroke; Nomogram; Prognosis; Radiomics.

Fig. 1. Workflow of radiomics analysis.

ADC = apparent diffusion coefficient, DWI = diffusion-weighted imaging, ICC = interclass correlation coefficient, LASSO = least absolute shrinkage and selection operator, mRMR = minimum redundancy maximum relevance, ROC = receiver operating characteristic

Fig. 2. ROC curves of the radiomics model, clinical model, and clinical-radiomics model in the training (A) and validation (B) cohorts.

ROC = receiver operating characteristic

Fig. 3. The clinical-radiomics nomogram for predicting acute ischemic stroke outcomes.

A. The developed nomogram based on the clinical-radiomics prediction model to predict the risk of poor stroke outcome. Diabetes: 0, no diabetes; 1, diabetes. Sex: 0, female; 1, male. Stroke history: 0, no stroke history; 1, stroke history; mRS_baseline: 0, ≤ 2; 1, > 2. B. Calibration curves for the nomogram in the training and validation cohorts. The green dashed line represents the ideal prediction and the red dashed line represents the predictive ability of the nomogram. The closer the dashed red line fit to the dashed green line, the greater the prediction accuracy of the nomogram. C. Decision curve analysis for the nomogram. The black line represents the net benefit of assuming no stroke patients have poor outcomes. The purple line is the net benefit of assuming all stroke patients have poor outcome. The orange line, green line, and blue line represent the expected net benefit of predicting stroke outcome using the clinical-radiomics model, clinical model, and radiomics model respectively. mRS_baseline = baseline modified Rankin Scale score, NIHSS_baseline = baseline National Institutes of Health Stroke Scale score