Objective To evaluate the imaging features of hepatic epithelioid hemangioendothelioma (HEHE). Methods The imaging data of 15 patients with HEHE proved by surgery and pathology who reeived treatment in West China Hospital from Jul. 2012 to Aug. 2018, were retrospectively analyzed. The location, boundary, density/signal, and enhanced features of tumor were observed. Results Among 15 cases, there were 3 cases of single, 5 cases of multiple, and 6 cases of fusion. Thirteen cases were distributed under the capsular of liver, accompanied by the capsule retraction sign, 14 cases had lollipop sign, 7 cases had core pattern sign. On plain CT images, the lesions manifested as low density. On plain MR images, the lesions had hypointense on T1-weighted images and hyperintense on T2-weighted images. The enhanced scanning could be characterized by mild enhancement, rim-like enhancement at early phase, and progressive centripetal fill-in enhancement during dynamic phase imaging. Conclusions CT and MRI imagings of HEHE are different, and there are certain characteristics of capsule retraction sign, lollipop sign, and core pattern sign.
Objective To determine feasibility of texture analysis of CT images for the discrimination of hepatic epithelioid hemangioendothelioma (HEHE) and liver metastases of colon cancer. Methods CT images of 9 patients with 19 pathologically proved HEHEs and 18 patients with 38 liver metastases of colon cancer who received treatment in West China Hospital of Sichuan University from July 2012 to August 2016 were retrospectively analyzed. Results Thirty best texture parameters were automatically selected by the combination of Fisher coefficient (Fisher)+classification error probability combined with average correlation coefficients (PA)+mutual information (MI). The 30 texture parameters of arterial phase (AP) CT images were distributed in co-occurrence matrix (22 parameters), run-length matrix (1 parameter), histogram (4 parameters), gradient (1 parameter), and autoregressive model (2 parameters). The distribution of parameters in portal venous phase (PVP) were co-occurrence matrix (18 parameters), run-length matrix (2 parameters), histogram (7 parameters), gradient (2 parameters), and autoregressive model (1 parameter). In AP, the misclassification rates of raw data analysis (RDA)/K nearest neighbor classification (KNN), principal component analysis (PCA)/KNN, linear discriminant analysis (LDA)/KNN, and nonlinear discriminant analysis, and nonlinear discriminant analysis (NDA)/artificial neural network (ANN) was 38.60% (22/57), 42.11% (24/57), 8.77% (5/57), and 7.02% (4/57), respectively. In PVP, the misclassification rates of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN was 26.32% (15/57), 28.07% (16/57), 15.79% (9/57), and 10.53% (6/57), respectively. The misclassification rates of AP and PVP images had no statistical significance on the misclassification rates of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN between AP and PVP (P>0.05). Conclusion The texture analysis of CT images is feasible to identify HEHE and liver metastases of colon cancer.
This article presented readers with typical enhanced CT and MR images of a patient with epithelioid hemangioendothelioma, and briefly described the pathological mechanisms behind the typical imaging signs, in order to enhance the readers’ understanding and awareness of the typical imaging signs of this rare disease, and thus reduce its underdiagnosis rate and misdiagnosis rate.