“… 19 The pattern is easy to recognise by pathologists with excellent interobserver agreement (kappa values 0.823-0.912) reported consistently across several studies both on H&E-stained and immunohistochemistry-stained sections. 19 , [23] , [24] , [25] In our experience, the addition of immunohistochemistry enables the VETC pattern to be screened for more rapidly at low magnification than on H&E sections. Fig.…”
Section: Nomenclature Definition and Prevalence Of Vetcmentioning
confidence: 98%
“…Compositionally, VETC consists of endothelial cells (CD31 or CD34 positive), hepatic stellate cells (alpha-smooth muscle actin positive), and basement membrane, 11 similar to that of capillary vessels. While it is possible to recognise the VETC pattern on routine H&E-stained sections, 23 almost all studies highlight the endothelium with immunohistochemical staining using anti-CD34 antibody. VETC can also be easily distinguished using antibodies against CD31 (five studies) or CD105 (one study).…”
Section: Nomenclature Definition and Prevalence Of Vetcmentioning
confidence: 99%
“… 24 Indeed, the reported VETC prevalence among studies of Japanese patients undergoing curative resection (using a cut-off of any VETC seen) is 8.7-23.8% which is lower than that seen in other (mostly Chinese) studies. 23 , 24 , 26 Whether this observation is related to race or the aetiology of liver disease (predominately chronic hepatitis C infection in Japanese cohorts vs . chronic hepatitis B infection in Chinese cohorts) is currently unclear.…”
Section: Nomenclature Definition and Prevalence Of Vetcmentioning
confidence: 99%
“…There are three Japanese cohorts (predominantly patients with hepatitis C) which did not demonstrate VETC to be an independent predictor of recurrence-free survival or OS on multivariable analyses. 23 , 24 , 26 A Taiwanese cohort study showed that the presence or absence of the VETC pattern was associated with significantly different disease-free survival in patients with hepatitis B but not hepatitis C infections. 34 …”
Section: Prognostic Value Of Vetc In Hccmentioning
“… 19 The pattern is easy to recognise by pathologists with excellent interobserver agreement (kappa values 0.823-0.912) reported consistently across several studies both on H&E-stained and immunohistochemistry-stained sections. 19 , [23] , [24] , [25] In our experience, the addition of immunohistochemistry enables the VETC pattern to be screened for more rapidly at low magnification than on H&E sections. Fig.…”
Section: Nomenclature Definition and Prevalence Of Vetcmentioning
confidence: 98%
“…Compositionally, VETC consists of endothelial cells (CD31 or CD34 positive), hepatic stellate cells (alpha-smooth muscle actin positive), and basement membrane, 11 similar to that of capillary vessels. While it is possible to recognise the VETC pattern on routine H&E-stained sections, 23 almost all studies highlight the endothelium with immunohistochemical staining using anti-CD34 antibody. VETC can also be easily distinguished using antibodies against CD31 (five studies) or CD105 (one study).…”
Section: Nomenclature Definition and Prevalence Of Vetcmentioning
confidence: 99%
“… 24 Indeed, the reported VETC prevalence among studies of Japanese patients undergoing curative resection (using a cut-off of any VETC seen) is 8.7-23.8% which is lower than that seen in other (mostly Chinese) studies. 23 , 24 , 26 Whether this observation is related to race or the aetiology of liver disease (predominately chronic hepatitis C infection in Japanese cohorts vs . chronic hepatitis B infection in Chinese cohorts) is currently unclear.…”
Section: Nomenclature Definition and Prevalence Of Vetcmentioning
confidence: 99%
“…There are three Japanese cohorts (predominantly patients with hepatitis C) which did not demonstrate VETC to be an independent predictor of recurrence-free survival or OS on multivariable analyses. 23 , 24 , 26 A Taiwanese cohort study showed that the presence or absence of the VETC pattern was associated with significantly different disease-free survival in patients with hepatitis B but not hepatitis C infections. 34 …”
Section: Prognostic Value Of Vetc In Hccmentioning
“…Furthermore, HCCs expressing the vascular-associated gene cluster may exhibit this histopathological feature [14,18]. For example, two independent studies showed that 90% and 78.4% of the VETC type accompanied the MTM type [14,19].…”
Macrotrabecular‐massive hepatocellular carcinoma (MTM‐HCC) is a subtype of HCC with a very poor prognosis and exhibits biological behaviors and clinical presentations that distinguish it from conventional HCC. Vessels encapsulating tumor clusters (VETC) is a unique pattern of vascular growth that is more common in patients with MTM‐HCC. This unique interaction between the angiogenic system and tumor cells is of general interest. Early diagnosis and appropriate treatment of HCC with MTM and VETC patterns help prevent early recurrence and improve prognosis. This review summarizes the existing findings for HCC with MTM and VETC patterns.
BackgroundThe metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC‐related radiological studies still focus on the prediction of VETC status.PurposeThis study aimed to build and compare VETC‐MVI related models (clinical, radiomics, and deep learning) associated with recurrence‐free survival of HCC patients.Study TypeRetrospective.Population398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22–80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40).Field Strength/Sequence3‐T, pre‐contrast T1‐weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2‐weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP).AssessmentTwo radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical‐radiomic (CR) nomogram, deep learning model. The follow‐up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow‐up. Patients were followed up until December 31, 2022.Statistical TestsUnivariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan–Meier curves, log‐rank test, C‐index, and area under the curve (AUC). P < 0.05 was considered statistically significant.ResultsThe C‐index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1–80 months).Data ConclusionMR deep learning model based on VETC and MVI provides a potential tool for survival assessment.Evidence Level3Technical EfficacyStage 3
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