Journal Article

Defining Delayed Perihematomal Edema Expansion in Intracerebral Hemorrhage: Segmentation, Time Course, Risk Factors and Clinical Outcome

Zhang, Qinghua, Yao, Jianhua, Li, Zhaojian, Chen, Shengpan, Chang, Jianbo, Chen, Yihao, Wei, Junji, Feng, Ming, Qin, Chenchen, Ye, Zeju, Ma, Wenbin, Liu, Yixun, Tian, Fengxuan, Wang, Renzhi

2022

Overview

We attempt to generate a definition of delayed perihematomal edema expansion (DPE) and analyze its time course, risk factors, and clinical outcomes. A multi-cohort data was derived from the Chinese Intracranial Hemorrhage Image Database (CICHID). A non-contrast computed tomography (NCCT) -based deep learning model was constructed for fully automated segmentation hematoma and perihematomal edema (PHE). Time course of hematoma and PHE evolution correlated to initial hematoma volume was volumetrically assessed. Predictive values for DPE were calculated through receiver operating characteristic curve analysis and were tested in an independent cohort. Logistic regression analysis was utilized to identify risk factors for DPE formation and poor outcomes. The test cohort’s Dice scores of lesion segmentation were 0.877 and 0.642 for hematoma and PHE, respectively. Overall, 1201 patients were enrolled for time-course analysis of ICH evolution. A total of 312 patients were further selected for DPE analysis. Time course analysis showed the growth peak of PHE approximately concentrates in 14 days after onset. The best cutoff for DPE to predict poor outcome was 3.34 mL of absolute PHE expansion from 4-7 days to 8-14 days (AUC=0.784, sensitivity=72.2%, specificity=81.2%), and 3.78 mL of absolute PHE expansion from 8-14 days to 15-21 days (AUC=0.682, sensitivity=59.3%, specificity=92.1%) in the derivation sample. Patients with DPE was associated with worse outcome (OR: 12.340, 95%CI: 6.378-23.873, P<0.01), and the larger initial hematoma volume (OR: 1.021, 95%CI: 1.000-1.043, P=0.049) was the significant risk factor for DPE formation. This study constructed a well-performance deep learning model for automatic segmentations of hematoma and PHE. A new definition of DPE was generated and is confirmed to be related to poor outcomes in ICH. Patients with larger initial hematoma volume have a higher risk of developing DPE formation.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Brain Edema - diagnostic imaging / Brain Edema - etiology / Cerebral Hemorrhage - diagnostic imaging / Computed tomography / Deep learning / delayed perihematomal edema expansion / Edema / Hematoma / Hematoma - diagnostic imaging / Hematoma - etiology / Hemorrhage / Humans / Hypertension / Immunology / intracerebral hemorrhage / Medical research / Missing data / Performance evaluation / prognosis / Risk Factors / Sample size / Segmentation / Software / Stroke / time course