Review
Copyright ©The Author(s) 2022.
World J Gastroenterol. Jul 14, 2022; 28(26): 3008-3026
Published online Jul 14, 2022. doi: 10.3748/wjg.v28.i26.3008
Table 1 World Health Organization Classification and grading criteria for neuroendocrine neoplasms of the gastrointestinal tract and hepatopancreatobiliary organs (7)
Terminology
Differentiation
Grade
Mitoses/2 mm2
Ki-67 index
NEN grade 1Well differentiatedLow< 2< 3%
NEN grade 2Intermediate2-203%-20%
NEN grade 3High> 20> 20%
SCNECPoorly differentiatedHigh1> 20> 20%
LCNEC> 20> 20%
MiNENWell or poorly differentiatedVariableVariableVariable
Table 2 Sensitivity of common imaging modalities used in the evaluation of gastroenteropancreatic neuroendocrine neoplasms
Imaging modality
Sensitivity
Transabdominal USG13%-27% for GEPNEN
Contrast enhanced ultrasound99% in detecting liver metastases
Endoscopic ultrasonography 82%-93% for PNEN
CECT63%-82% for PNEN
CE MRI79% for PNEN
DWI83% for liver metastases
Table 3 Summary of important research studies on imaging of gastroenteropancreatic neuroendocrine neoplasms

Ref.
Number (n)
Modality
Results
Conclusion
UltrasonographyTakada et al[25], 201930Contrast-enhanced harmonic EUSThree parameters in TIC showed high diagnostic accuracy: Echo intensity change - 87%; Rate of enhancement - 87%; Enhancement ratio for node/pancreatic parenchyma - 88.5%Contrast-enhanced EUS and TIC analysis show high diagnostic accuracy for grading of PNEN
CTWorhunsky et al[45], 2014118APCT5-year overall survival: Hypoenhancing - 54%; Isoenhancing - 89%; Hyperenhancin - 93%. On multivariate analysis only hypoenhancement (HR 2.32, P = 0.02) was independently associated with survivalHypoenhancement of PNEN on APCT (22% of well-differentiated PNEN) was an independent predictor of poor outcome
Rodallec et al[46], 200637Dual-phase contrast-enhanced CTPoorly differentiated NEC: Hypoattenuating - 71%; Isoattenuating or weakly hyperattenuating - 29%; Well-differentiated NECmoderately or strongly hyperattenuating - 53%. Poor enhancement at pancreatic phase and less vascularized tumors were associated with decreased survival rateEnhancement of PNEN at CT correlated with microscopic tumor vascularity. Low-enhancing PNEN correlated with poor differentiation and lower overall survival
Park et al[48], 202069Dynamic CTNEC (compared to well-differentiated NEN): Significantly higher frequencies of main pancreatic ductal dilatation, bile duct dilatation, vascular invasion; Significantly lower conspicuity of interface between tumor and parenchyma, AER and PER. PER < 0.8 showed 94.1% sensitivity, 88.5% specificity for differentiation of NEC from well-differentiated NEN. On combining 3 significant CT features, the sensitivity and specificity for diagnosing NEC were 88.2% and 88.5% respectivelyTumor parenchyma enhancement ratio in portal phase is useful to distinguish NECs from well differentiated NENs. Combining qualitative and quantitative CT features aid in achieving good diagnostic accuracy in differentiation between NEC and well-differentiated NEN
d’Assignies et al[59], 200936MDCT perfusionTumor blood flow and intratumoral MVD showed high correlation (r = 0.620, P < 0.001). Blood flow was significantly higher in: Grade 1 than grade 2/3 tumors; Tumors with proliferation index ≤ 2% (P = 0.005); Tumors without histological signs of microscopic vascular involvement (P = 0.008). Mean transit time was longer in tumors with lymph node (P = 0.02) or liver (P = 0.05) metastasisPerfusion CT is feasible in patients with pancreatic NENs and reflects MVD. Perfusion CT measurements correlated with histoprognostic factors, such as proliferation index and WHO grading
MRICanellas et al[103], 201880MRI MRI features associated with aggressive tumors: Size > 2 cm (OR = 4.8); T2 non-bright lesions (OR = 4.6); Presence of pancreatic ductal dilatation (OR = 4.9); Diffusion restriction (OR = 4.9)MRI can assess aggressiveness of PNEN and identify patients at risk for early disease progression after surgical resection
d’Assignies et al[74], 201359MRI DWI (71%-71.6%) was more sensitive than T2 weighted images (55.6%) and dynamic CEMRI (47.5%-48.1%). Combination of these sequences improved detection of liver metastases. Specificity of each sequence was comparable (89%-100%)DWI is more sensitive for detection and characterization of liver metastases from NENs than T2-weighted and dynamic gadolinium-enhanced MRI
Radiomics, texture analysis and machine learningCanellas et al[103], 2018101CECT with texture analysisCT features predictive of a more aggressive tumor: Size > 2 cm (OR = 3.3); Vascular involvement (OR = 25.2); Pancreatic ductal dilatation (OR = 6); Lymphadenopathy (OR = 6.8); Entropy (OR = 3.7); Differences (P < 0.05) in progression free survival were found for: Grade 1 vs grade 2 vs grade 3 tumors; PNEN with vascular involvement; Tumors with entropy values > 4.65CT texture analysis and CT features are predictive of aggressiveness and can be used to identify patients at risk of early disease progression after surgical resection
De Robertis et al[77], 201842MRI and histogram analysisADC entropy is significantly higher in grade 2/3 tumors (sensitivity: 83.3%, specificity: 61.1%). ADC kurtosis is higher in PNENs with vascular involvement, nodal and hepatic metastases (sensitivity: 85.7%, specificity: 74.3%)Whole tumor ADC histogram analysis can predict aggressiveness in PNENs. ADC entropy and ADC kurtosis are the most accurate parameters for identification of PNEN with malignant behavior
Luo et al[112], 202093CECT with application of a CNN based DL algorithmAUC = 0.81 of arterial phase in validation set was significantly higher than those of venous (AUC = 0.57, P = 0.03) and arterial/venous phase (AUC = 0.70, P = 0.03) in predicting the pathological grading of PNENs. The AUC and accuracy of DL algorithm for diagnosing grade 3 PNEN were 0.80% and 79.1%. There was significant difference in OS and PFS between the predicted G1/2 and G3 groupsThe CNN-based DL method showed a relatively robust performance in predicting pathological grading of PNENs from CECT images
Gao et al[114], 201996CEMRI with application of deep learning algorithm on imagesThe average accuracy of the five trained CNNs ranged between 79.08% and 82.35%, and the range of micro- average AUC was between 0.8825 and 0.8932. The average accuracy and micro-average AUC of the averaged CNN were 81.05% and 0.8847 respectivelyWith the help of GAN, the CNN showed the potential to predict the grades of PNENs on CEMRI