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European Radiology

, Volume 27, Issue 4, pp 1748–1759 | Cite as

Prediction of pancreatic neuroendocrine tumour grade with MR imaging features: added value of diffusion-weighted imaging

  • Emad Lotfalizadeh
  • Maxime RonotEmail author
  • Mathilde Wagner
  • Jérôme Cros
  • Anne Couvelard
  • Marie-Pierre Vullierme
  • Wassim Allaham
  • Olivia Hentic
  • Philippe Ruzniewski
  • Valérie Vilgrain
Gastrointestinal

Abstract

Objectives

To evaluate the value of MR imaging including diffusion-weighted imaging (DWI) for the grading of pancreatic neuroendocrine tumours (pNET).

Material and Methods

Between 2006 and 2014, all resected pNETs with preoperative MR imaging including DWI were included. Tumour grading was based on the 2010 WHO classification. MR imaging features included size, T1-w, and T2-w signal intensity, enhancement pattern, apparent (ADC) and true diffusion (D) coefficients.

Results

One hundred and eight pNETs (mean 40 ± 33 mm) were evaluated in 94 patients (48 women, 51 %, mean age 52 ± 12). Fifty-five (51 %), 42 (39 %), and 11 (10 %) tumours were given the following grades (G): G1, G2, and G3. Mean ADC and D values were significantly lower as grade increased (ADC: 2.13 ± 0.70, 1.78 ± 0.72, and 0.86 ± 0.22 10-3 mm2/s, and D: 1.92 ± 0.70, 1.75 ± 0.74, and 0.82 ± 0.19 10-3 mm2/s G1, G2, and G3, all p < 0.001). A higher grade was associated with larger sized tumours (p < 0.001). The AUROC of ADC and D to differentiate G3 and G1-2 were 0.96 ± 0.02 and 0.95 ± 0.02. Optimal cut-off values for the identification of G3 were 1.19 10-3 mm2/s for ADC (sensitivity 100 %, specificity 92 %) and 1.04 10-3 mm2/s for D (sensitivity 82 %, specificity 92 %).

Conclusion

Morphological/functional MRI features of pNETS depend on tumour grade. DWI is useful for the identification of high-grade tumours.

Key Points

Morphological and functional MRI features of pNETs depend on tumour grade.

Their combination has a high predictive value for grade.

All pNETs should be explored by MR imaging including DWI.

DWI is helpful for identification of high-grade and poorly-differentiated tumours.

Keywords

Neoplasm Pancreas Ki-67 Carcinoma Grading 

Abbreviations

ADC

apparent diffusion coefficient

D

true diffusion coefficient

DWI

diffusion-weighted imaging

ENETS

European Neuroendocrine Tumour Society

G

grade

(p)NET

(pancreatic) neuroendocrine tumour

MRI

magnetic resonance imaging

WHO

World Health Organization

Notes

Acknowledgments

The scientific guarantor of this publication is Maxime Ronot. 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. The authors state that this work has not received any funding. No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Methodology: retrospective, diagnostic or prognostic study, performed at one institution.

Supplementary material

330_2016_4539_Fig6_ESM.gif (26 kb)
Supplemental Figure 1

Example of a G1 pNET of the head of the pancreas in a 58 yo female. MR imaging showed a small (< 2 cm) hypervascular tumour on arterial phase images (arrow in a) with high signal intensity on high b value diffusion-weighted images (b=600 s/mm2) (arrow in b), and a mean ADC value of 2.30 10-3 mm2/s (c). All features were consistent with a G1 tumour. The lesion was resected, and pathological analysis confirmed the tumour grade (Ki67 < 1%). (GIF 26 kb)

330_2016_4539_Fig7_ESM.gif (23 kb)
Supplemental Figure 1

Example of a G1 pNET of the head of the pancreas in a 58 yo female. MR imaging showed a small (< 2 cm) hypervascular tumour on arterial phase images (arrow in a) with high signal intensity on high b value diffusion-weighted images (b=600 s/mm2) (arrow in b), and a mean ADC value of 2.30 10-3 mm2/s (c). All features were consistent with a G1 tumour. The lesion was resected, and pathological analysis confirmed the tumour grade (Ki67 < 1%). (GIF 26 kb)

330_2016_4539_Fig8_ESM.gif (34 kb)
Supplemental Figure 1

Example of a G1 pNET of the head of the pancreas in a 58 yo female. MR imaging showed a small (< 2 cm) hypervascular tumour on arterial phase images (arrow in a) with high signal intensity on high b value diffusion-weighted images (b=600 s/mm2) (arrow in b), and a mean ADC value of 2.30 10-3 mm2/s (c). All features were consistent with a G1 tumour. The lesion was resected, and pathological analysis confirmed the tumour grade (Ki67 < 1%). (GIF 26 kb)

330_2016_4539_MOESM1_ESM.tif (307 kb)
High resolution image (TIF 306 kb)
330_2016_4539_MOESM2_ESM.tif (249 kb)
High resolution image (TIF 248 kb)
330_2016_4539_MOESM3_ESM.tif (371 kb)
High resolution image (TIF 370 kb)
330_2016_4539_Fig9_ESM.gif (21 kb)
Supplemental Figure 2

Proposition of classification algorithm for the prediction of tumour grade. The right hand side column represents the mean (and standard deviation) of each box. (GIF 21 kb)

330_2016_4539_MOESM4_ESM.tiff (182 kb)
High resolution image (TIFF 181 kb)
330_2016_4539_MOESM5_ESM.docx (16 kb)
ESM 1 (DOCX 15 kb)

References

  1. 1.
    Zerbi A, Falconi M, Rindi G et al (2010) Clinicopathological features of pancreatic endocrine tumors: a prospective multicenter study in Italy of 297 sporadic cases. Am J Gastroenterol 105:1421–1429CrossRefPubMedGoogle Scholar
  2. 2.
    Kloppel G (2011) Classification and pathology of gastroenteropancreatic neuroendocrine neoplasms. Endocr-Relat Cancer 18:S1–S16CrossRefPubMedGoogle Scholar
  3. 3.
    Klimstra DS, Modlin IR, Coppola D, Lloyd RV, Suster S (2010) The pathologic classification of neuroendocrine tumors: a review of nomenclature, grading, and staging systems. Pancreas 39:707–712CrossRefPubMedGoogle Scholar
  4. 4.
    Kloppel G, Rindi G, Perren A, Komminoth P, Klimstra DS (2010) The ENETS and AJCC/UICC TNM classifications of the neuroendocrine tumors of the gastrointestinal tract and the pancreas: a statement. Virchows Arch Int J Pathol 456:595–597CrossRefGoogle Scholar
  5. 5.
    Plockinger U, Rindi G, Arnold R et al (2004) Guidelines for the diagnosis and treatment of neuroendocrine gastrointestinal tumours. A consensus statement on behalf of the European Neuroendocrine Tumour Society (ENETS). Neuroendocrinology 80:394–424CrossRefPubMedGoogle Scholar
  6. 6.
    Scarpa A, Mantovani W, Capelli P et al (2010) Pancreatic endocrine tumors: improved TNM staging and histopathological grading permit a clinically efficient prognostic stratification of patients. Mod Pathol 23:824–833CrossRefPubMedGoogle Scholar
  7. 7.
    Bettini R, Boninsegna L, Mantovani W et al (2008) Prognostic factors at diagnosis and value of WHO classification in a mono-institutional series of 180 non-functioning pancreatic endocrine tumours. Ann Oncol 19:903–908CrossRefPubMedGoogle Scholar
  8. 8.
    Ramage JK, Ahmed A, Ardill J et al (2012) Guidelines for the management of gastroenteropancreatic neuroendocrine (including carcinoid) tumours (NETs). Gut 61:6–32CrossRefPubMedGoogle Scholar
  9. 9.
    Bilimoria KY, Tomlinson JS, Merkow RP et al (2007) Clinicopathologic features and treatment trends of pancreatic neuroendocrine tumors: analysis of 9,821 patients. J Gastrointest Surg 11:1460–1467, discussion 1467–1469 Google Scholar
  10. 10.
    Alexiev BA, Darwin PE, Goloubeva O, Ioffe OB (2009) Proliferative rate in endoscopic ultrasound fine-needle aspiration of pancreatic endocrine tumors: correlation with clinical behavior. Cancer 117:40–45PubMedGoogle Scholar
  11. 11.
    Rebours V, Cordova J, Couvelard A et al (2015) Can pancreatic neuroendocrine tumour biopsy accurately determine pathological characteristics? Dig Liver Dis. doi: 10.1016/j.dld.2015.06.005 PubMedGoogle Scholar
  12. 12.
    Weynand B, Borbath I, Bernard V et al (2014) Pancreatic neuroendocrine tumour grading on endoscopic ultrasound-guided fine needle aspiration: high reproducibility and inter-observer agreement of the Ki-67 labelling index. Cytopathol Off J Br Soc Clin Cytol 25:389–395Google Scholar
  13. 13.
    Hasegawa T, Yamao K, Hijioka S et al (2014) Evaluation of Ki-67 index in EUS-FNA specimens for the assessment of malignancy risk in pancreatic neuroendocrine tumors. Endoscopy 46:32–38CrossRefPubMedGoogle Scholar
  14. 14.
    Bettini R, Partelli S, Boninsegna L et al (2011) Tumor size correlates with malignancy in nonfunctioning pancreatic endocrine tumor. Surgery 150:75–82CrossRefPubMedGoogle Scholar
  15. 15.
    Manfredi R, Bonatti M, Mantovani W et al (2013) Non-hyperfunctioning neuroendocrine tumours of the pancreas: MR imaging appearance and correlation with their biological behaviour. Eur Radiol 23:3029–3039CrossRefPubMedGoogle Scholar
  16. 16.
    Cappelli C, Boggi U, Mazzeo S et al (2015) Contrast enhancement pattern on multidetector CT predicts malignancy in pancreatic endocrine tumours. Eur Radiol 25:751–759CrossRefPubMedGoogle Scholar
  17. 17.
    Koito K, Namieno T, Nagakawa T, Morita K (1997) Delayed enhancement of islet cell carcinoma on dynamic computed tomography: a sign of its malignancy. Abdom Imaging 22:304–306CrossRefPubMedGoogle Scholar
  18. 18.
    d'Assignies G, Couvelard A, Bahrami S et al (2009) Pancreatic endocrine tumors: tumor blood flow assessed with perfusion CT reflects angiogenesis and correlates with prognostic factors. Radiology 250:407–416CrossRefPubMedGoogle Scholar
  19. 19.
    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 25:1375–1383CrossRefPubMedGoogle Scholar
  20. 20.
    Rodallec M, Vilgrain V, Couvelard A et al (2006) Endocrine pancreatic tumours and helical CT: contrast enhancement is correlated with microvascular density, histoprognostic factors and survival. Pancreatology 6:77–85CrossRefPubMedGoogle Scholar
  21. 21.
    Kim DW, Kim HJ, Kim KW et al (2016) Prognostic value of CT findings to predict survival outcomes in patients with pancreatic neuroendocrine neoplasms: a single institutional study of 161 patients. Eur Radiol 26:1320–1329CrossRefPubMedGoogle Scholar
  22. 22.
    Kim JH, Eun HW, Kim YJ, Lee JM, Han JK, Choi BI (2016) Pancreatic neuroendocrine tumour (PNET): Staging accuracy of MDCT and its diagnostic performance for the differentiation of PNET with uncommon CT findings from pancreatic adenocarcinoma. Eur Radiol 26:1338–1347CrossRefPubMedGoogle Scholar
  23. 23.
    Park HS, Kim SY, Hong SM et al (2016) Hypervascular solid-appearing serous cystic neoplasms of the pancreas: differential diagnosis with neuroendocrine tumours. Eur Radiol 26:1348–1358CrossRefPubMedGoogle Scholar
  24. 24.
    Barral M, Sebbag-Sfez D, Hoeffel C et al (2013) Characterization of focal pancreatic lesions using normalized apparent diffusion coefficient at 1.5-Tesla: preliminary experience. Diagn Interv Imaging 94:619–627CrossRefPubMedGoogle Scholar
  25. 25.
    Kang KM, Lee JM, Yoon JH, Kiefer B, Han JK, Choi BI (2014) Intravoxel incoherent motion diffusion-weighted MR imaging for characterization of focal pancreatic lesions. Radiology 270:444–453CrossRefPubMedGoogle Scholar
  26. 26.
    Brenner R, Metens T, Bali M, Demetter P, Matos C (2012) Pancreatic neuroendocrine tumor: added value of fusion of T2-weighted imaging and high b-value diffusion-weighted imaging for tumor detection. Eur J Radiol 81:e746–e749CrossRefPubMedGoogle Scholar
  27. 27.
    d'Assignies G, Fina P, Bruno O et al (2013) High sensitivity of diffusion-weighted MR imaging for the detection of liver metastases from neuroendocrine tumors: comparison with T2-weighted and dynamic gadolinium-enhanced MR imaging. Radiology 268:390–399CrossRefPubMedGoogle Scholar
  28. 28.
    Wang Y, Chen ZE, Yaghmai V et al (2011) Diffusion-weighted MR imaging in pancreatic endocrine tumors correlated with histopathologic characteristics. J Magn Reson Imaging 33:1071–1079CrossRefPubMedGoogle Scholar
  29. 29.
    Jang KM, Kim SH, Lee SJ, Choi D (2014) The value of gadoxetic acid-enhanced and diffusion-weighted MRI for prediction of grading of pancreatic neuroendocrine tumors. Acta Radiol 55:140–148CrossRefPubMedGoogle Scholar
  30. 30.
    Pereira JA, Rosado E, Bali M, Metens T, Chao SL (2015) Pancreatic neuroendocrine tumors: correlation between histogram analysis of apparent diffusion coefficient maps and tumor grade. Abdom Imaging 40:3122–3128CrossRefPubMedGoogle Scholar
  31. 31.
    Birnbaum DJ, Gaujoux S, Cherif R et al (2014) Sporadic nonfunctioning pancreatic neuroendocrine tumors: prognostic significance of incidental diagnosis. Surgery 155:13–21CrossRefPubMedGoogle Scholar
  32. 32.
    Gaujoux S, Partelli S, Maire F et al (2013) Observational study of natural history of small sporadic nonfunctioning pancreatic neuroendocrine tumors. J Clin Endocrinol Metab 98:4784–4789CrossRefPubMedGoogle Scholar
  33. 33.
    Kishi Y, Shimada K, Nara S, Esaki M, Hiraoka N, Kosuge T (2014) Basing treatment strategy for non-functional pancreatic neuroendocrine tumors on tumor size. Ann Surg Oncol 21:2882–2888CrossRefPubMedGoogle Scholar
  34. 34.
    Hwang EJ, Lee JM, Yoon JH et al (2014) Intravoxel incoherent motion diffusion-weighted imaging of pancreatic neuroendocrine tumors: prediction of the histologic grade using pure diffusion coefficient and tumor size. Invest Radiol 49:396–402CrossRefPubMedGoogle Scholar
  35. 35.
    Luo Y, Dong Z, Chen J et al (2014) Pancreatic neuroendocrine tumours: correlation between MSCT features and pathological classification. Eur Radiol 24:2945–2952CrossRefPubMedGoogle Scholar
  36. 36.
    Gratian L, Pura J, Dinan M, Roman S, Reed S, Sosa JA (2014) Impact of extent of surgery on survival in patients with small nonfunctional pancreatic neuroendocrine tumors in the United States. Ann Surg Oncol 21:3515–3521CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Klau M, Mayer P, Bergmann F et al (2015) Correlation of histological vessel characteristics and diffusion-weighted imaging intravoxel incoherent motion-derived parameters in pancreatic ductal adenocarcinomas and pancreatic neuroendocrine tumors. Invest Radiol 50:792–797CrossRefPubMedGoogle Scholar
  38. 38.
    Surov A, Caysa H, Wienke A, Spielmann RP, Fiedler E (2015) Correlation between different ADC fractions, cell count, Ki-67, total nucleic areas and average nucleic areas in meningothelial meningiomas. Anticancer Res 35:6841–6846PubMedGoogle Scholar
  39. 39.
    Karaman A, Durur-Subasi I, Alper F et al (2015) Correlation of diffusion MRI with the Ki-67 index in non-small cell lung cancer. Radiol Oncol 49:250–255CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Yao JC, Hassan M, Phan A et al (2008) One hundred years after “carcinoid”: epidemiology of and prognostic factors for neuroendocrine tumors in 35,825 cases in the United States. J Clin Oncol 26:3063–3072CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Emad Lotfalizadeh
    • 1
  • Maxime Ronot
    • 1
    • 2
    • 3
    Email author
  • Mathilde Wagner
    • 1
    • 3
  • Jérôme Cros
    • 2
    • 4
  • Anne Couvelard
    • 2
    • 4
  • Marie-Pierre Vullierme
    • 1
  • Wassim Allaham
    • 1
  • Olivia Hentic
    • 5
  • Philippe Ruzniewski
    • 5
  • Valérie Vilgrain
    • 1
    • 2
    • 3
  1. 1.Department of RadiologyUniversity Hospitals Paris Nord Val de SeineClichyFrance
  2. 2.University Paris DiderotParisFrance
  3. 3.INSERM U1149, Centre de Recherche Biomédicale Bichat-Beaujon, CRB3ParisFrance
  4. 4.Department of PathologyUniversity Hospitals Paris Nord Val de SeineClichyFrance
  5. 5.Department of GastroenterologyUniversity Hospitals Paris Nord Val de SeineClichyFrance

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