Subregional non-contrast CT radiomics features based on habitat imaging technology for predicting hematoma expansion in patients with spontaneous intracranial hemorrhage
10.13929/j.issn.1003-3289.2023.12.011
- VernacularTitle:以生境成像技术提取血肿内亚区域平扫CT影像组学特征预测自发性颅内出血患者血肿增大
- Author:
Wanjun LU
1
;
Mengxuan YUAN
;
Jian PENG
;
Chengtuan SUN
;
Jieling SHEN
;
Liqing GAO
Author Information
1. 扬州大学附属江都人民医院神经内科,江苏扬州 225200
- Keywords:
cerebral hemorrhage;
hematoma;
radiomics;
tomography,X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2023;39(12):1792-1797
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of subregional non-contrast CT(NCCT)radiomics features based on habitat imaging technology for predicting hematoma expansion(HE)in patients with spontaneous intracranial hemorrhage(sICH).Methods Data of 228 sICH patients with negative conventional imaging signs were retrospectively analyzed and divided into HE group(n=99)or non HE(NHE)group(n=129)based on the occurrence of HE nor not.also divided into training set(n=182)or test set(n=46)at a ratio of 8:2.Clinical data,NCCT data and laboratory examination results were compared between groups.Logistic regressive analysis was performed to screen the impact factors of HE.ROI of whole hematoma(ROIwhole)was sketched and clustered into 3 sub-regions(ROIsub1,ROIsub2 and ROIsub3,the latter located in the critical area between hematoma and brain tissue)with habitat imaging technology,and radiomics features of ROI were extracted and screened.Then 4 prediction models were constructed based on the above 4 ROI,and the efficacy of each model for predicting HE was analyzed.Results The fasting blood glucose in HE group was higher than that in NHE group(t=2.047,P=0.041),which was not independent impact factor for predicting HE in sICH patients(P=0.070)according to logistic regression analysis.The area under the curve of ROIsub3 radiomics model for predicting sICH HE in training and test set was 0.945 and 0.863,respectively,not significantly different with that of ROIwhole(0.921,0.813),ROIsub1(0.925,0.807)nor ROIsub2(0.909,0.720)(all P＞0.05).Decision curve analysis showed that ROIsub3 radiomics model could bring greater benefits than the other 3 models.Conclusion NCCT radiomics features of the critical area between hematoma and brain tissue based on habitat imaging technology had high value for predicting HE in sICH patients.
