Prediction of hemorrhagic transformation in acute ischemic stroke patients using clinico-radiomics models

Abstract

This study aimed to develop and validate a clinico-radiomics model integrating radiomics features from multiparametric MRI and clinical scoring systems to predict hemorrhagic transformation (HT) in acute ischemic stroke. A total of 918 patients were retrospectively included. Patients from Institution A who underwent MRIs between 2017 and 2019 were assigned to the training set (n = 792), whereas those from 2020 (n = 78) formed the internal validation set. External validation included 48 patients from Institution B. All patients underwent multiparametric MRI, including diffusion- and perfusion-weighted imaging, fluid-attenuated inversion recovery, and gradient-echo. Radiomics features were selected using the least absolute shrinkage and selection operator regression and random forest models. Clinico-radiomics models were developed using logistic regression by combining radiomics features with clinical scoring systems (HAT, SEDAN, DRAGON, SITS-ICH). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), with DeLong's test for comparison. Ten radiomics features were selected: time-to-peak, mean transit time, time-to-maximum, relative cerebral blood volume, and relative cerebral blood flow. The radiomics model demonstrated comparable performance to clinical models in the internal validation set (AUC: 0.79 vs. 0.75-0.81); but outperformed them in the external validation set (AUC: 0.85 vs. 0.66-0.68). Clinico-radiomics models demonstrated higher AUCs than radiomics or clinical models in both internal (AUC: 0.81-0.84 vs. 0.79 vs. 0.75-0.81) and external validation sets (AUC: 0.86-0.89 vs. 0.85 vs. 0.66-0.68). These findings suggest that clinico-radiomics models offer improved predictive accuracy for HT compared to radiomics or clinical scoring models alone.

Keywords: Acute ischemic stroke; Hemorrhagic transformation; Magnetic resonance imaging; Perfusion; Radiomics.

Fig. 1

Patients inclusion process. MRI = magnetic resonance imaging; DWI = diffusion-weighted imaging; FLAIR = fluid attenuated inversion recovery; GRE = gradient echo; PWI = perfusion-weighted imaging;

Fig. 2

Coefficients of the optimal radiomics features. the above graph shows the regression coefficients calculated using LASSO, selected features with non-zero coefficients. Features with larger absolute values have a greater impact on predicting the target variable (hemorrhagic transformation). Positive coefficients indicate a positive influence, while negative coefficients indicate a negative influence. The below graph represents feature importance derived from Random Forest based on Gini impurity. Values closer to 1 indicate a more significant role in decision-making. The top 10 most important features are displayed.

Fig. 3

Schematic flow of the study. ROI = region of interest; LASSO = least absolute shrinkage and selection operator; DM = diabetes mellitus; NIHSS = national institutes of health stroke scale; mRS = modified Rankin scale; sBP = systolic blood pressure; HTN = hypertension.

Fig. 4

Predictive performance of the radiomics, clinical, and clinico-radiomics models. (A) Receiver operating characteristic (ROC) curve and DeLong test p-value matrix for the training set. (B) ROC curve and DeLong test p-value matrix for the internal validation set. (C) ROC curve and DeLong test p-value matrix for the external validation set. CV = cross-validation; HAT = hemorrhage after thrombolysis; SEDAN = blood sugar, early infarct signs, hyperdense cerebral artery sign, age; DRAGON = diabetes, race, age, glucose, onset to treatment time, national institutes of health stroke scale (NIHSS) score; SITS-ICH = safe implementation of treatments in stroke–international collaboration on hemorrhagic transformation.