Dynamic enhanced CT: is there a difference between liver metastases of gastroenteropancreatic neuroendocrine tumor and adenocarcinoma

Abstract

This study proposed to evaluate the feasibility of dynamic enhanced CT in differentiation of liver metastases of gastroenteropancreatic well-differentiated neuroendocrine tumors (GEP NETs) from GEP adenocarcinomas based on their characteristic features. CT images of 23 well-differentiated (G1 or G2) GEP NETs and 23 GEP adenocarcinomas patients with liver metastases were retrospectively reviewed. The distribution type, shape, intra-tumoral neovascularity, enhancement on hepatic artery phase, dynamic enhancement pattern and lymphadenopathy were subjective analyzed. Meanwhile, the size, number, CT value of tumor and adjacent normal liver parenchyma were measured and the metastasis-to-liver ratios were calculated objectively. Compared with GEP adenocarcinomas, the liver metastases of GEP NETs more frequently demonstrated a hyper enhancement on hepatic artery phase, washout dynamic enhancement pattern, absence of lymphadenopathy and higher metastasis-to-liver ratios on both hepatic artery phase and portal venous phase (P=0.017, P<0.001, P =0.038, P <0.001 and P =0.008, respectively). Logistic regression analysis showed that the dynamic enhancement pattern (P=0.012), and the metastasis-to-liver ratios on hepatic artery phase (P=0.009) were independent CT predictors for liver metastases of GEP NETs. The sensitivity and specificity of combing the two predictors in differentiation of liver metastases of GEP adenocarcinomas from GEP NET were 82.6% (19 of 23) and 91.3% (21 of 23), respectively. CT features are helpful in differentiating liver metastases of well-differentiated GEP NETs from that of GEP adenocarcinomas.

Recomendaciones para el diagnóstico y el tratamiento de tumores neuroendocrinos de origen gastroenteropancreáticos

“…[27][28][29] En cuanto a las lesiones hepáticas, el patrón de realce y el porcentaje de lesiones con realce en fase arterial demostraron ser predictores independientes de secundarismo de origen neuroendocrino (versus adenocarcinoma). 30 Además, el comportamiento de las lesiones con contraste EV dinámico permite sugerir el grado de diferenciación del TNE-GEP (G1 y G2 suelen ser hipervascularizados). 31 La RM es de preferencia en aquellos pacientes candidatos a terapias ablativas o citorreductivas hepáticas, debido a su mayor sensibilidad y especificidad para detectar lesiones en el parénquima hepático.…”

Imaging of pancreatic neuroendocrine tumors: recent advances, current status, and controversies

“…Recent studies also have reported the ability of CT to differentiate between liver metastases due to a panNETs/GI-NETs from those due to a GI adenocarcinoma[59]; its ability to predict recurrence after resection of panNETs[60]; and its ability to predict which patients will develop pancreatic fistulas post resection of a panNET[61]. A recent study reported that liver metastases from panNETs/GI-NETs can be best distinguished from liver metastases from a GI-adenocarcinomas on CT scanning by assessing the dynamic enhancement pattern(p=0.012) and the metastases to liver ratios on the hepatic artery phase(p=0.009)[59]. Both were found to be independent predictors for liver metastases from NETs with the sensitivity and specificity of the combined two predictors being 83% and 91%, respectively[59].…”

“…A recent study reported that liver metastases from panNETs/GI-NETs can be best distinguished from liver metastases from a GI-adenocarcinomas on CT scanning by assessing the dynamic enhancement pattern(p=0.012) and the metastases to liver ratios on the hepatic artery phase(p=0.009)[59]. Both were found to be independent predictors for liver metastases from NETs with the sensitivity and specificity of the combined two predictors being 83% and 91%, respectively[59]. In another recent study[60] the contrast-enhancement ratio(CER) of panNETs calculated from the multiphase enhanced CT scan was determined [CER=CT attenuation of the tumor from the maximum enhancing phase divided by the pre-enhanced phase value] and found to be a useful predictor of disease recurrence post resection of the panNET[60].…”

Machine learning-based identification of contrast-enhancement phase of computed tomography scans

“…In fact, dynamic CT plays an essential role in the diagnosis, treatment, and response assessment of hepatocellular carcinoma based on the widely utilized Liver Imaging Reporting and Data System (LI-RADS) [6]. However, not only are there significant differences in HCC lesion detection amongst the various phases of dynamic CT [7,8], but different phases also provide different diagnostic accuracy for different types of lesions [9,10]. In addition, certain phase-specific findings can be highly informative, such as hypervascularity of HCC lesions in the arterial phase being predictive of malignancy [11].…”