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Comparison between neuroendocrine carcinomas and well-differentiated neuroendocrine tumors of the pancreas using dynamic enhanced CT

European Radiology volume 30, pages 4772–4782 (2020)Cite this article

Abstract

Objectives

To identify CT features distinguishing neuroendocrine carcinomas (NECs) of pancreas from well-differentiated neuroendocrine tumors (NETs) according to the World Health Organization 2017 and 2019 classification systems.

Methods

This retrospective study included 69 patients with pathologically confirmed pancreatic neuroendocrine neoplasms who underwent dynamic CT (17, 17, 18, and 17 patients for well-differentiated grade 1, 2, 3 NET and NEC, respectively). CT was used to perform qualitative analysis (component, homogeneity, calcification, peripancreatic infiltration, main pancreatic ductal dilatation, bile duct dilatation, intraductal extension, and vascular invasion) and quantitative analysis (interface between tumor and parenchyma [delta], arterial enhancement ratio [AER], portal enhancement ratio [PER], and dynamic enhancement pattern). Uni- and multivariate logistic regression analyses were performed to identify features indicating NEC. Optimal cutoff values for enhancement ratios were determined.

Results

NECs demonstrated significantly higher frequencies of main pancreatic ductal dilatation, bile duct dilatation, vascular invasion, and significantly lower delta (i.e., lower conspicuity), AER, and PER than well-differentiated NET (p < 0.05). On multivariate analysis, PER was the only independent factor selected by the model for differentiation of NEC from well-differentiated NET (odds ratio, < 0.001; 95% confidence interval [CI], < 0.001–0.012). PER < 0.8 showed the sensitivity of 94.1% (95% CI, 71.3–99.9) and the specificity of 88.5% (95% CI, 76.6–95.6). When three significant CT features were combined, the sensitivity and specificity for diagnosing NEC were 88.2% and 88.5%, respectively.

Conclusions

Tumor-parenchyma enhancement ratio in portal phase is a useful CT feature to distinguish NECs from well-differentiated NETs. Combining qualitative and quantitative CT features may aid in achieving good diagnostic accuracy in the differentiation between NEC and well-differentiated NET.

Key Points

• Neuroendocrine carcinoma of the pancreas should be distinguished from well-differentiated neuroendocrine tumor in line with the revised grading and staging system.

• Neuroendocrine carcinoma of the pancreas can be differentiated from well-differentiated neuroendocrine tumor on dynamic CT based on assessment of the portal enhancement ratio, arterial enhancement ratio, tumor conspicuity, dilatation of the main pancreatic duct or bile duct, and vascular invasion.

• Tumor-parenchyma enhancement ratio in portal phase of dynamic CT is a useful feature, which may help to distinguish neuroendocrine carcinoma from well-differentiated neuroendocrine tumor of the pancreas.

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Abbreviations

AER:

Arterial enhancement ratio

G1:

Grade 1

G2:

Grade 2

G3:

Grade 3

HU:

Hounsfield unit

NEC:

Neuroendocrine carcinoma

NET:

Neuroendocrine tumor

PanNEN:

Pancreatic neuroendocrine neoplasm

PD:

Poorly differentiated

PER:

Portal enhancement ratio

ROC:

Receiver operating characteristics

ROI:

Region of interest

WD:

Well differentiated

WHO:

World Health Organization

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Acknowledgments

Authors would like to express their appreciation to Dr. Yu Sub Sung for his assistance in measuring delta and making Fig. 2.

Funding

The authors state that this work has not received any funding.

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Affiliations

  1. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea

    Hyo Jung Park, Hyoung Jung Kim, Kyung Won Kim, So Yeon Kim & Sang Hyun Choi

  2. Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea

    Myung-Won You

  3. Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Hee Sang Hwang & Seung-Mo Hong

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  1. Hyo Jung Park
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  2. Hyoung Jung Kim
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Corresponding author

Correspondence to Hyoung Jung Kim.

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Guarantor

The scientific guarantor of this publication is Hyoung Jung Kim.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in:

a. Kim DW, Kim HJ, Kim KW, et al (2015) Neuroendocrine neoplasms of the pancreas at dynamic enhanced CT: comparison between grade 3 neuroendocrine carcinoma and grade 1/2 neuroendocrine tumour. Eur Radiol 25:1375–1383.

b. Kim JY, Kim MS, Kim KS, et al (2015) Clinicopathologic and prognostic significance of multiple hormone expression in pancreatic neuroendocrine tumours. Am J Surg Pathol 39:592–601.

c. Son EM, Kim JY, An S et al (2015) Clinical and prognostic significances of cytokeratin 19 and KIT expression in surgically resectable pancreatic neuroendocrine tumors. J Pathol Transl Med 49:30–36.

d. Hwang HS, Kim Y, An S et al (2018) Grading by the Ki-67 labeling index of endoscopic ultrasound-guided fine-needle aspiration biopsy specimens of pancreatic neuroendocrine tumors can be underestimated. Pancreas 47:1296–1303.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Park, H.J., Kim, H.J., Kim, K.W. et al. Comparison between neuroendocrine carcinomas and well-differentiated neuroendocrine tumors of the pancreas using dynamic enhanced CT. Eur Radiol 30, 4772–4782 (2020). https://doi.org/10.1007/s00330-020-06867-w

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  • DOI: https://doi.org/10.1007/s00330-020-06867-w

Keywords

  • Pancreas
  • Carcinoma
  • Neuroendocrine
  • Neuroendocrine tumors
  • Multidetector computed tomography