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

Korean Journal of Radiology 2021년 22권 3호 p.415 ~ 424
Song Zuhua, Guo Dajing, Tang Zhuoyue, Liu Huan, Li Xin, Luo Sha, Yao Xueying, Song Wenlong, Song Junjie, Zhou Zhiming,
소속 상세정보
 ( Song Zuhua ) - Chongqing Medical University Second Affiliated Hospital Department of Radiology
 ( Guo Dajing ) - Chongqing Medical University Second Affiliated Hospital Department of Radiology
 ( Tang Zhuoyue ) - Chongqing General Hospital Radiology Department
 ( Liu Huan ) - GE Healthcare
 ( Li Xin ) - Chongqing Medical University Second Affiliated Hospital Department of Radiology
 ( Luo Sha ) - Chongqing Medical University Second Affiliated Hospital Department of Radiology
 ( Yao Xueying ) - Chongqing Medical University Second Affiliated Hospital Department of Radiology
 ( Song Wenlong ) - Chongqing Medical University Second Affiliated Hospital Department of Radiology
 ( Song Junjie ) - Chongqing Medical University Second Affiliated Hospital Department of Radiology
 ( Zhou Zhiming ) - Chongqing Medical University Second Affiliated Hospital Department of Radiology

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.

키워드

Hematoma expansion; Radiomics; Machine learning; Intracerebral hemorrhage; Computed tomography

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