Noncontrast Computed Tomography-Based Radiomics Analysis in Discriminating Early Hematoma Expansion after Spontaneous Intracerebral Hemorrhage

Abstract

Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion: NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.

Radiomics for predicting revised hematoma expansion with the inclusion of intraventricular hemorrhage growth in patients with supratentorial spontaneous intraparenchymal hematomas

“…Furthermore, we compared the ability of 5 classic ML algorithms, LR, SVM, RF, GBDT, and NB, in constructing the RM to discriminate RHE after supratentorial sICH. As the tree-based ensemble model appeared to be overfitted on the training set, the regularized LR algorithm achieves a more robust performance in this study and the result is consistent with a previous study (48).…”

Location-Specific Radiomics Score: Novel Imaging Marker for Predicting Poor Outcome of Deep and Lobar Spontaneous Intracerebral Hemorrhage

“…Normally, the radiomics score (Rad-score) is developed based on the optimal radiomics features, which may comprehensively reflect the nature of lesions. Recently, several studies have reported that the Rad-score shows better performance than traditional imaging markers in predicting HE or poor outcomes after SICH ( Xie et al, 2019 ; Song et al, 2021a , b ). However, these recent studies do not take into account the potential differences in Rad-scores among different haematoma locations, which may be helpful for decision-making regarding the individualized treatment of SICH.…”

Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH‐2 trial intracerebral hemorrhage population

“…In this study, we devised and validated hematoma radiomics signa- Radiomics analysis has been widely applied in oncologic imaging for molecular subtyping, survival prognostication, and prediction of treatment response [8,[21][22][23]. Recent studies suggested that hematoma radiomics features can predict the likelihood of hematoma expansion [24][25][26][27]. In this study, we showed that while hematoma volume and radiomic shape features had strong association with severity of baseline clinical presentation and b R. R. Wilcox percentile bootstrap method for comparing dependent robust correlations [28].…”