Digestive Diseases and Sciences

, Volume 63, Issue 11, pp 3147–3152 | Cite as

Simple Vascular Architecture Classification in Predicting Pancreatic Neuroendocrine Tumor Grade and Prognosis

  • Ke Chen
  • Wenming Zhang
  • Zhaozhen Zhang
  • Yiping He
  • Yuan Liu
  • Xiujiang YangEmail author
Original Article


Background and Aim

Vascularity is a critical feature in the evaluation of pancreatic neuroendocrine tumor (PNET). When done by EUS, contrast agents are recommended. However, vascular architecture (VA) can also be evaluated by routine Doppler flow in EUS without contrast agents. Our aim was to provide a simple VA classification in EUS for PNET grade and prognosis.


All pathologically proven PNET cases with EUS between 2012 and 2018 were retrospectively analyzed. The Doppler imaging was retrieved for VA classification. Predictive model construction was performed by machine learning algorithms.


A total of 112 PNET cases were evaluated, among which 93 cases were subjected to VA classification. The VA was classified into type A (peritumoral with or without intratumoral vessels [A1 or A2]); type B (only intratumoral vessels); and type C (flow was absent). The VA classification was significantly correlated with tumor grades: 74% type A1 was G1, 73% type B was G2, and 58% type C was G3. Multivariate analysis indicated that elevated serum CA19-9 and type C classification were the independent predictors of G3 tumor. Five machine learning models were constructed, among which random forest was the best one with an AUC of 0.9972. Low-risk patients classified by this model exhibited better prognosis than high-risk patients (p = 0.0087).


In the novel simple VA classification, peritumoral, intratumoral, and absent vessels are prone to be G1, G2, and G3, respectively. Combined with serum CA19-9 and lesion size, the VA classification could predict tumor grade and prognosis in PNET.


Pancreatic neuroendocrine tumor Endosonographic Staging Tumor grade Prognosis 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Supplementary material

10620_2018_5240_MOESM1_ESM.tif (5.5 mb)
Supplemental Fig. 1. Bar graph of the variable importance of each item for the random forest model (TIFF 5630 kb)


  1. 1.
    Adil MT, Nagaraja R, Varma V, et al. A single centre analysis of clinical characteristics and treatment of endocrine pancreatic tumours. Int J Surg Oncol. 2015;2015:538948.PubMedPubMedCentralGoogle Scholar
  2. 2.
    Dasari A, Shen C, Halperin D, et al. Trends in the Incidence, prevalence, and survival outcomes in patients with neuroendocrine tumors in the United States. JAMA Oncol. 2017;3:1335–1342.CrossRefGoogle Scholar
  3. 3.
    Alexandraki KI, Kaltsas G. Gastroenteropancreatic neuroendocrine tumors: new insights in the diagnosis and therapy. Endocrine. 2012;41:40–52.CrossRefGoogle Scholar
  4. 4.
    Deng B-Y, Liu F, Yin S-N, et al. Clinical outcome and long-term survival of 150 consecutive patients with pancreatic neuroendocrine tumors: a comprehensive analysis by the World Health Organization 2010 grading classification. Clin Res Hepatol Gastroenterol. 2018;42:261–268.CrossRefGoogle Scholar
  5. 5.
    Kloppel G. Neuroendocrine neoplasms: dichotomy, origin and classifications. Visc Med. 2017;33:324–330.CrossRefGoogle Scholar
  6. 6.
    Han X, Xu X, Ma H, et al. Clinical relevance of different WHO grade 3 pancreatic neuroendocrine neoplasms based on morphology. Endocr Connect. 2018;7:355–363.CrossRefGoogle Scholar
  7. 7.
    Lotfalizadeh E, Ronot M, Wagner M, et al. Prediction of pancreatic neuroendocrine tumour grade with MR imaging features: added value of diffusion-weighted imaging. Eur Radiol. 2017;27:1748–1759.CrossRefGoogle Scholar
  8. 8.
    Kang J, Ryu JK, Son JH, et al. Association between pathologic grade and multiphase computed tomography enhancement in pancreatic neuroendocrine neoplasm. J Gastroenterol Hepatol. 2018.Google Scholar
  9. 9.
    De Robertis R, Cingarlini S, Tinazzi Martini P, et al. Pancreatic neuroendocrine neoplasms: magnetic resonance imaging features according to grade and stage. World J Gastroenterol. 2017;23:275–285.CrossRefGoogle Scholar
  10. 10.
    Khashab MA, Yong E, Lennon AM, et al. EUS is still superior to multidetector computerized tomography for detection of pancreatic neuroendocrine tumors. Gastrointest Endosc. 2011;73:691–696.CrossRefGoogle Scholar
  11. 11.
    Puli SR, Kalva N, Bechtold ML, et al. Diagnostic accuracy of endoscopic ultrasound in pancreatic neuroendocrine tumors: a systematic review and meta analysis[J]. World J Gastroenterol. 2013;19:3678–3684.CrossRefGoogle Scholar
  12. 12.
    James PD, Tsolakis AV, Zhang M, et al. Incremental benefit of preoperative EUS for the detection of pancreatic neuroendocrine tumors: a meta-analysis. Gastrointest Endosc. 2015;81:848–856.CrossRefGoogle Scholar
  13. 13.
    Fujimori N, Osoegawa T, Lee L, et al. Efficacy of endoscopic ultrasonography and endoscopic ultrasonography-guided fine-needle aspiration for the diagnosis and grading of pancreatic neuroendocrine tumors. Scand J Gastroenterol. 2016;51:245–252.CrossRefGoogle Scholar
  14. 14.
    Palazzo M, Napoléon B, Gincul R, et al. Contrast harmonic EUS for the prediction of pancreatic neuroendocrine tumor aggressiveness (with videos). Gastrointest Endosc. 2018;87:1481–1488.CrossRefGoogle Scholar
  15. 15.
    Ishikawa T, Itoh A, Kawashima H, et al. Usefulness of EUS combined with contrast-enhancement in the differential diagnosis of malignant versus benign and preoperative localization of pancreatic endocrine tumors. Gastrointest Endosc. 2010;71:951–959.CrossRefGoogle Scholar
  16. 16.
    Iordache S, Angelescu R, Filip MM, et al. Power Doppler endoscopic ultrasound for the assessment of pancreatic neuroendocrine tumors. Endosc Ultrasound. 2012;1:150–155.CrossRefGoogle Scholar
  17. 17.
    Pais SA, Al-Haddad M, Mohamadnejad M, et al. EUS for pancreatic neuroendocrine tumors: a single-center, 11-year experience. Gastrointest Endosc. 2010;71:1185–1193.CrossRefGoogle Scholar
  18. 18.
    Lv Y, Han X, Zhang C, et al. Combined test of serum CgA and NSE improved the power of prognosis prediction of NF-pNETs. Endocr Connect. 2018;7:169–178.CrossRefGoogle Scholar
  19. 19.
    Hijioka M, Ito T, Igarashi H, et al. Serum chromogranin A is a useful marker for Japanese patients with pancreatic neuroendocrine tumors. Cancer Sci. 2014;105:1464–1471.CrossRefGoogle Scholar
  20. 20.
    Hallemeier CL, Botros M, Corsini MM, et al. Preoperative CA 19-9 level is an important prognostic factor in patients with pancreatic adenocarcinoma treated with surgical resection and adjuvant concurrent chemoradiotherapy. Am J Clin Oncol. 2011;34:567–572.CrossRefGoogle Scholar
  21. 21.
    Luo G, Jin K, Cheng H, et al. Carbohydrate antigen 19-9 as a prognostic biomarker in pancreatic neuroendocrine tumors. Oncol Lett. 2017;14:6795–6800.CrossRefGoogle Scholar
  22. 22.
    Konukiewitz B, Jesinghaus M, Steiger K, et al. Pancreatic neuroendocrine carcinomas reveal a closer relationship to ductal adenocarcinomas than to neuroendocrine tumors G3. Hum Pathol. 2018.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ke Chen
    • 1
    • 2
  • Wenming Zhang
    • 1
    • 2
  • Zhaozhen Zhang
    • 1
    • 2
  • Yiping He
    • 1
    • 2
  • Yuan Liu
    • 1
    • 2
  • Xiujiang Yang
    • 1
    • 2
    Email author
  1. 1.Department of EndoscopyFudan University Shanghai Cancer CenterShanghaiChina
  2. 2.Department of OncologyShanghai Medical College, Fudan UniversityShanghaiChina

Personalised recommendations