Contrast-enhanced MDCT in patients with pancreatic neuroendocrine tumours: correlation with histological findings and diagnostic performance in differentiation between tumour grades

Abstract

AIM To identify the multidetector computed tomography (MDCT) features of pancreatic neuroendocrine tumours (pNETs), which correlate with tumour histology and enable preoperative grading. MATERIALS AND METHODS Thirty-nine patients with histologically confirmed pNET who underwent preoperative contrast-enhanced MDCT were included in this study. Nineteen tumours were classified as Grade 1 (G1) and 20 as Grade 2 (G2). Histopathology slides were reviewed to assess the intratumoural microvascular density (MVD) and the amount of tumour stroma. Computed tomography (CT) image analysis included tumour size, margin delineation, calcifications, homogeneity, contrast enhancement (CE) pattern, tumour absolute and relative enhancement, presence of cystic changes, pancreatic duct dilatation, regional and distant metastases. The diagnostic ability to predict tumour grade was measured for each MDCT finding and their combinations. RESULTS The mean arterial enhancement ratio had a mean±standard deviation of 1.53±0.45 in G1 and 1.01±0.33 in G2 pNETs (p=0.0003) and correlated with intratumoural microvascular density (MVD; r=0.55, p=0.0002). Tissue stroma percentage did not correlate with imaging findings. Late CE of the tumour (the peak attenuation observed in the venous phase) was significantly associated with G2. Tumour size ≥20 mm, arterial enhancement ratio <1.1, and late CE showed 74.4%, 79.5%, and 74.4% accuracy, respectively, in diagnosing G2 tumours, while the accuracy of at least two of these criteria used in combination was 82%. Based on these results, a diagnostic algorithm was proposed, which showed high interobserver agreement (k=0.82) in the prediction of tumour grade. CONCLUSION Contrast-enhanced MDCT features correlate with histological findings and enable the differentiation between G1 and G2 pNETs during preoperative examination.

Well Differentiated Grade 3 Neuroendocrine Tumors of the Digestive Tract: A Narrative Review

“…The development of the NET test, a multianalyte liquid biopsy that measures NET gene expression, has shown promising results in diagnosis and predictive therapeutic assessment, and might also help individualize NET G−3. Some works have shown that radiology, such as contract-enhanced multidetector computed tomography, can help distinguish pathological grades, in particular G−1 from G−2 pancreatic NET [89]. Future radiological studies might allow to help distinguish NET G−3 from NEC or NET G−3.…”

Utility of CT in differentiating liver metastases of well-differentiated gastroenteropancreatic neuroendocrine neoplasms from poorly-differentiated neuroendocrine neoplasms

“…Due to the excellent spatial and time resolution of multi-detector CT (MDCT), it is possible to obtain information on tumor characteristics related to pathologic grades. Recent studies have reported that specific CT features can help to differentiate poorlydifferentiated gastric NENs from well-differentiated NENs, and are useful in the pathological classification of pancreatic NENs (11)(12)(13)(14). However, to the best of our knowledge, no published studies have yet focused on comparing the CT features of the LMs of well-and poorlydifferentiated GEP-NENs.…”

The impact of 18F-FDOPA-PET/MRI image fusion in detecting liver metastasis in patients with neuroendocrine tumors of the gastrointestinal tract

“…While DWI alone is less sensitive than a gadoxetic acid-enhanced MRI in detecting liver metastases, it increases the sensitivity of detection for liver metastases to 90.6-95.5%, when combined with DCE-MRI [35]. [36]. Third generation DECT, with an assessment of iodine uptake, improves the differentiation of hepatic NET metastasis and hepatocellular carcinoma (HCC) in noncirrhotic livers, as shown in a study by Kaltenbach et al that examined 46 patients with either hepatic NEN metastasis or HCC, all of whom had undergone liver DECT in a retrospective study [37].…”