AUTHOR=Zhao Mingpei , Huang Wei , Huang Shuna , Lin Fuxin , He Qiu , Zheng Yan , Gao Zhuyu , Cai Lveming , Ye Gengzhao , Chen Renlong , Wu Siying , Fang Wenhua , Wang Dengliang , Lin Yuanxiang , Kang Dezhi , Yu Lianghong TITLE=Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage JOURNAL=Frontiers in Neurology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.999223 DOI=10.3389/fneur.2022.999223 ISSN=1664-2295 ABSTRACT=Background

Early hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth.

Methods

We used data from the Risk Stratification and Minimally Invasive Surgery in Acute Intracerebral Hemorrhage (Risa-MIS-ICH) trial. Our study included patients with ICH with a time to baseline NCCT <12 h and a follow-up CT duration <72 h. To get a Hounsfield unit histogram and the coefficient of variation (CV) of Hounsfield units (HUs), the hematoma was segmented by software using the auto-segmentation function. Quantitative hematoma heterogeneity is represented by the CV of hematoma HUs. Multivariate logistic regression was utilized to determine hematoma growth parameters. The discriminant score predictive value was assessed using the area under the ROC curve (AUC). The best cutoff was determined using ROC curves. Hematoma growth was defined as a follow-up CT hematoma volume increase of >6 mL or a hematoma volume increase of 33% compared with the baseline NCCT.

Results

A total of 158 patients were enrolled in the study, of which 31 (19.6%) had hematoma growth. The multivariate logistic regression analysis revealed that time to initial baseline CT (P = 0.040, odds ratio [OR]: 0.824, 95 % confidence interval [CI]: 0.686–0.991), “heterogeneous” in the density category (P = 0.027, odds ratio [OR]: 5.950, 95 % confidence interval [CI]: 1.228–28.828), and CV of hematoma HUs (P = 0.018, OR: 1.301, 95 % CI: 1.047–1.617) were independent predictors of hematoma growth. By evaluating the receiver operating characteristic curve, the CV of hematoma HUs (AUC = 0.750) has a superior predictive value for hematoma growth than for heterogeneous density (AUC = 0.638). The CV of hematoma HUs had an 18% cutoff, with a specificity of 81.9 % and a sensitivity of 58.1 %.

Conclusion

The CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently.