A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage - Xu, Wei; Guo, Hongquan; Li, Huiping; Dai, Qiliang; Song, Kangping; Li, Fangyi; Zhou, Junjie; Yao, Jingjiang; Wang, Zhen; Liu, Xinfeng (Frontiers in Neurology, 2022)
Background and Purpose: Hematoma expansion (HE) is a critical event following acute intracerebral hemorrhage (ICH). We aimed to construct a non-contrast computed tomography (NCCT) model combining clinical characteristics, radiological signs and radiomics features to predict HE in patients with spontaneous ICH, and to develop a nomogram to assess the risk of early HE. Materials and Methods: We retrospectively reviewed 388 patients with ICH who underwent initial NCCT within 6 hours after onset and follow-up CT within 24 hours after initial NCCT, between January 2015 and December 2021. Using LASSO algorithm or stepwise logistic regression analysis, five models (clinical model, radiological model, clinical-radiological model, radiomics model, and combined model) were developed to predict HE in the training cohort (n=235), and independently verified in the test cohort (n=153). The Akaike information criterion (AIC) and likelihood ratio test (LRT) were used for comparing the goodness of ﬁt of the five models, and the AUC was used to evaluate their ability in discriminating HE. A nomogram was developed based on the model with the best performance. Results: The combined model (AIC=202.599, χ2=80.6) was the best fitting model with the lowest AIC and the highest LRT chi-square value compared to the clinical model (AIC=232.263, χ2=46.940), radiological model (AIC=227.932, χ2=51.270), clinical-radiological model (AIC=212.711, χ2=55.490) or radiomics model (AIC=217.647, χ2=57.550). In both cohorts, the nomogram derived from the combined model showed satisfactory discrimination and calibration for predicting HE (AUC=0.900, sensitivity=83.87%; AUC=0.850, sensitivity=80.10%, respectively). Conclusions: The NCCT-based model combining clinical characteristics, radiological signs and radiomics features could efficiently discriminate early HE, and the nomogram derived from the combined model, as a non-invasive tool, exhibited a satisfactory performance in stratifying HE risks.