Qian Chen1, Dongqin Zhu1, Jinjin Liu1, Mingyue Zhang1, Haoli Xu1, Yilan Xiang1, Chenyi Zhan1, Yong Zhang2, Shengwei Huang3, Yunjun Yang4. 1. Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang Province, China. 2. Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China. 3. Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China. 4. Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang Province, China. Electronic address: yyjunjim@163.com.
Abstract
RATIONALE AND OBJECTIVES: Noncontrast CT-based radiomics signature has shown ability for detecting hematoma expansion (HE) in spontaneous intracerebral hemorrhage (ICH). We sought to compare its predictive performance with clinical risk factors and develop a clinical-radiomics nomogram to assess the risk of early HE. MATERIALS AND METHODS: In total, 1153 patients with ICH who underwent baseline cranial CT within 6 hours and follow-up scans within 72 hours of stroke onset were enrolled, of whom 864 (75%) were assigned to the derivation cohort and 289 (25%) to the validation cohort. Based on LASSO algorithm or stepwise logistic regression analysis, three models (clinical model, radiomics model, and hybrid model) were constructed to predict HE. The Akaike information criterion (AIC) and likelihood ratio test (LRT) were used for comparing the goodness of fit of the three models, and the AUC was used to evaluate their discrimination ability for HE. RESULTS: The hybrid model (AIC = 681.426; χ2= 128.779) was the optimal model with the lowest AIC and highest chi-square values compared to the radiomics model (AIC = 767.979; χ2 = 110.234) or the clinical model (AIC = 753.757; χ2 = 56.448). The radiomics model was superior in the prediction of HE to the clinical model in both derivation (p = 0.009) and validation (p = 0.022) cohorts. In both datasets, the clinical-radiomics nomogram showed satisfactory discrimination and calibration for detecting HE (AUC = 0.771, Sensitivity = 87.0%; AUC = 0.820, Sensitivity = 88.1%; respectively). CONCLUSION: Among patients with acute ICH, noncontrast CT-based radiomics model outperformed the clinical-only model in the prediction of HE, and the established clinical-radiomics nomogram with favorable performance can offer a noninvasive tool for the risk stratification of HE.
RATIONALE AND OBJECTIVES: Noncontrast CT-based radiomics signature has shown ability for detecting hematoma expansion (HE) in spontaneous intracerebral hemorrhage (ICH). We sought to compare its predictive performance with clinical risk factors and develop a clinical-radiomics nomogram to assess the risk of early HE. MATERIALS AND METHODS: In total, 1153 patients with ICH who underwent baseline cranial CT within 6 hours and follow-up scans within 72 hours of stroke onset were enrolled, of whom 864 (75%) were assigned to the derivation cohort and 289 (25%) to the validation cohort. Based on LASSO algorithm or stepwise logistic regression analysis, three models (clinical model, radiomics model, and hybrid model) were constructed to predict HE. The Akaike information criterion (AIC) and likelihood ratio test (LRT) were used for comparing the goodness of fit of the three models, and the AUC was used to evaluate their discrimination ability for HE. RESULTS: The hybrid model (AIC = 681.426; χ2= 128.779) was the optimal model with the lowest AIC and highest chi-square values compared to the radiomics model (AIC = 767.979; χ2 = 110.234) or the clinical model (AIC = 753.757; χ2 = 56.448). The radiomics model was superior in the prediction of HE to the clinical model in both derivation (p = 0.009) and validation (p = 0.022) cohorts. In both datasets, the clinical-radiomics nomogram showed satisfactory discrimination and calibration for detecting HE (AUC = 0.771, Sensitivity = 87.0%; AUC = 0.820, Sensitivity = 88.1%; respectively). CONCLUSION: Among patients with acute ICH, noncontrast CT-based radiomics model outperformed the clinical-only model in the prediction of HE, and the established clinical-radiomics nomogram with favorable performance can offer a noninvasive tool for the risk stratification of HE.
Authors: Stefan P Haider; Adnan I Qureshi; Abhi Jain; Hishan Tharmaseelan; Elisa R Berson; Tal Zeevi; Shahram Majidi; Christopher G Filippi; Simon Iseke; Moritz Gross; Julian N Acosta; Ajay Malhotra; Jennifer A Kim; Lauren H Sansing; Guido J Falcone; Kevin N Sheth; Seyedmehdi Payabvash Journal: Eur J Neurol Date: 2021-07-18 Impact factor: 6.288