Endocrine Pathology

, Volume 26, Issue 3, pp 255–262 | Cite as

Comparison of Three Ki-67 Index Quantification Methods and Clinical Significance in Pancreatic Neuroendocrine Tumors

  • Trynda N. Kroneman
  • Jesse S. Voss
  • Christine M. Lohse
  • Tsung-Teh Wu
  • Thomas C. Smyrk
  • Lizhi ZhangEmail author


The Ki-67 index is essential in the pathological reports for pancreatic neuroendocrine tumors. There are three methods to determine the Ki-67 index including eyeball estimation, manual counting, or automated digital imaging analysis. The goal of this study was to compare the three quantification methods with the clinical outcome to determine the best method for clinical practice. Ki-67 immunostaining was performed on 97 resected pancreatic neuroendocrine tumors. The three methods of quantification were employed: (1) an average of eyeball estimation by three pathologists; (2) manual counting of at least 500 tumor cells; and (3) digital imaging analysis quantitation by selecting 8–10 hot spot regions. All tumors were graded according to the 2010 WHO grading system. The three quantification methods for the Ki-67 index had almost perfect agreement. The concordance between manual counting and digital imaging analysis and between manual counting and average eyeball estimation were 0.97 and 0.88, respectively. The concordance among the three pathologists’ eyeball estimation was 0.86. All three methods correlated with patients’ survival using the 2010 WHO grading system. Eyeball estimation scores were significantly less than those of the other two methods and tended to downgrade more tumors to grade 1, but they had higher predictive ability for survival and recurrence. The WHO system using the mitotic rate could also separate patients with different survival and even downgraded more tumors to grade 1. The results suggest the necessity of a consensus among pathologists for the method to determine the Ki-67 index and proper cutoff of the Ki-67 index for better clinical correlation.


Pancreatic neuroendocrine tumor Ki-67 Mitosis Digital imaging analysis Prognosis 


Conflict of Interest

The authors have no conflicts of interest or funding to disclose.


  1. 1.
    Ferrone CR, Tang LH, Tomlinson J, Gonen M, Hochwald SN, Brennan MF, et al. Determining prognosis in patients with pancreatic endocrine neoplasms: can the WHO classification system be simplified? J Clin Oncol. 2007;25(35):5609–15.PubMedCrossRefGoogle Scholar
  2. 2.
    Jamali M, Chetty R. Predicting prognosis in gastroentero-pancreatic neuroendocrine tumors: an overview and the value of Ki-67 immunostaining. Endocrine pathology. 2008;19(4):282–8.PubMedCrossRefGoogle Scholar
  3. 3.
    La Rosa S, Klersy C, Uccella S, Dainese L, Albarello L, Sonzogni A, et al. Improved histologic and clinicopathologic criteria for prognostic evaluation of pancreatic endocrine tumors. Hum Pathol. 2009;40(1):30–40.PubMedCrossRefGoogle Scholar
  4. 4.
    Scarpa A, Mantovani W, Capelli P, Beghelli S, Boninsegna L, Bettini R, et al. Pancreatic endocrine tumors: improved TNM staging and histopathological grading permit a clinically efficient prognostic stratification of patients. Mod Pathol. 2010;23(6):824–33.PubMedCrossRefGoogle Scholar
  5. 5.
    Hochwald SN, Zee S, Conlon KC, Colleoni R, Louie O, Brennan MF, et al. Prognostic factors in pancreatic endocrine neoplasms: an analysis of 136 cases with a proposal for low-grade and intermediate-grade groups. J Clin Oncol. 2002;20(11):2633–42.PubMedCrossRefGoogle Scholar
  6. 6.
    Capella C, Heitz PU, Hofler H, Solcia E, Kloppel G. Revised classification of neuroendocrine tumours of the lung, pancreas and gut. Virchows Arch. 1995;425(6):547–60.PubMedCrossRefGoogle Scholar
  7. 7.
    Zhang L, Lohse CM, Dao LN, Smyrk TC. Proposed histopathologic grading system derived from a study of KIT and CK19 expression in pancreatic endocrine neoplasm. Hum Pathol. 2011;42(3):324–31.PubMedCrossRefGoogle Scholar
  8. 8.
    DeLellis RA LR, Heitz PU, and Eng C. Pathology & Genetics, Tumors of Endocrine Organs 2004.Google Scholar
  9. 9.
    Rindi G, de Herder WW, O'Toole D, Wiedenmann B. Consensus guidelines for the management of patients with digestive neuroendocrine tumors: why such guidelines and how we went about It. Neuroendocrinology. 2006;84(3):155–7.PubMedCrossRefGoogle Scholar
  10. 10.
    Yamaguchi T, Fujimori T, Tomita S, Ichikawa K, Mitomi H, Ohno K, et al. Clinical validation of the gastrointestinal NET grading system: Ki67 index criteria of the WHO 2010 classification is appropriate to predict metastasis or recurrence. Diagn Pathol. 2013;8:65.PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Bosman F, Carneiro F, Hruban R, Theise N. WHO classification of tumours of the digestive system. Lyon: International Agency for Research on Cancer; 2010.Google Scholar
  12. 12.
    Adsay V. Ki67 labeling index in neuroendocrine tumors of the gastrointestinal and pancreatobiliary tract: to count or not to count is not the question, but rather how to count. The American journal of surgical pathology. 2012;36(12):1743–6.PubMedCrossRefGoogle Scholar
  13. 13.
    Klimstra DS, Modlin IR, Adsay NV, Chetty R, Deshpande V, Gönen M, Jensen RT, Kidd M, Kulke MH, Lloyd RV, Moran C, Moss SF, Oberg K, O'Toole D, Rindi G, Robert ME, Suster S, Tang LH, Tzen CY, Washington MK, Wiedenmann B, Yao J. Pathology Reporting of Neuroendocrine Tumors: Application of the Delphic Consensus Process to the Development of a Minimum Pathology Data Set. Am J Surg Pathol 2010;34(3):300–13PubMedCrossRefGoogle Scholar
  14. 14.
    Rindi G, Kloppel G, Alhman H, Caplin M, Couvelard A, de Herder WW, et al. TNM staging of foregut (neuro)endocrine tumors: a consensus proposal including a grading system. Virchows Arch. 2006;449(4):395–401.PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Reid MD, Bagci P, Ohike N, Saka B, Erbarut Seven I, Dursun N, et al. Calculation of the Ki67 index in pancreatic neuroendocrine tumors: a comparative analysis of four counting methodologies. Mod Pathol. 2015;28(5):686–94.PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Zhang L, Smyrk TC, Oliveira AM, Lohse CM, Zhang S, Johnson MR, et al. KIT is an Independent Prognostic Marker for Pancreatic Endocrine Tumors: A Finding Derived From Analysis of Islet Cell Differentiation Markers. Am J Surg Pathol. 2009;33(10):1562–9.PubMedCrossRefGoogle Scholar
  17. 17.
    Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45(1):255–68.PubMedCrossRefGoogle Scholar
  18. 18.
    Harrell FE, Jr., Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in medicine. 1996;15(4):361–87.PubMedCrossRefGoogle Scholar
  19. 19.
    McCall CM, Shi C, Cornish TC, Klimstra DS, Tang LH, Basturk O, et al. Grading of well-differentiated pancreatic neuroendocrine tumors is improved by the inclusion of both Ki67 proliferative index and mitotic rate. The American journal of surgical pathology. 2013;37(11):1671–7.PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Sorbye H, Strosberg J, Baudin E, Klimstra DS, Yao JC. Gastroenteropancreatic high-grade neuroendocrine carcinoma. Cancer. 2014;120(18):2814–23.PubMedCrossRefGoogle Scholar
  21. 21.
    Khan MS, Luong TV, Watkins J, Toumpanakis C, Caplin ME, Meyer T. A comparison of Ki-67 and mitotic count as prognostic markers for metastatic pancreatic and midgut neuroendocrine neoplasms. British journal of cancer. 2013;108(9):1838–45.PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    Tang LH, Gonen M, Hedvat C, Modlin IM, Klimstra DS. Objective quantification of the Ki67 proliferative index in neuroendocrine tumors of the gastroenteropancreatic system: a comparison of digital image analysis with manual methods. The American journal of surgical pathology. 2012;36(12):1761–70.PubMedCrossRefGoogle Scholar
  23. 23.
    Klapper W, Hoster E, Determann O, Oschlies I, van der Laak J, Berger F, et al. Ki-67 as a prognostic marker in mantle cell lymphoma-consensus guidelines of the pathology panel of the European MCL Network. Journal of Hematopathology. 2009;2(2):103–11.PubMedCentralPubMedCrossRefGoogle Scholar
  24. 24.
    Schwartz BR, Pinkus G, Bacus S, Toder M, Weinberg DS. Cell proliferation in non-Hodgkin's lymphomas. Digital image analysis of Ki-67 antibody staining. The American journal of pathology. 1989;134(2):327–36.PubMedCentralPubMedGoogle Scholar
  25. 25.
    Walts AE, Ines D, Marchevsky AM. Limited role of Ki-67 proliferative index in predicting overall short-term survival in patients with typical and atypical pulmonary carcinoid tumors. Mod Pathol. 2012; 25 (9): 1258–64.PubMedCrossRefGoogle Scholar
  26. 26.
    Yang Z, Tang LH, Klimstra DS. Effect of tumor heterogeneity on the assessment of Ki67 labeling index in well-differentiated neuroendocrine tumors metastatic to the liver: implications for prognostic stratification. Am J Surg Pathol. 2011;35(6):853–60.PubMedCrossRefGoogle Scholar
  27. 27.
    Nielsen PS, Riber-Hansen R, Raundahl J, Steiniche T. Automated Quantification of MART1-Verified Ki67 Indices by Digital Image Analysis in Melanocytic Lesions. Archives of pathology & laboratory medicine. 2012;136(6):627–34.CrossRefGoogle Scholar
  28. 28.
    Tuominen VJ, Ruotoistenmaki S, Viitanen A, Jumppanen M, Isola J. ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res. 2010;12(4):R56.PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Dhall D, Mertens R, Bresee C, Parakh R, Wang HL, Li M, et al. Ki-67 proliferative index predicts progression-free survival of patients with well-differentiated ileal neuroendocrine tumors. Hum Pathol. 2012;43(4):489–95.PubMedCrossRefGoogle Scholar
  30. 30.
    Goedkoop AY, de Rie MA, Teunissen MB, Picavet DI, van der Hall PO, Bos JD, et al. Digital image analysis for the evaluation of the inflammatory infiltrate in psoriasis. Archives of dermatological research. 2005;297(2):51–9.PubMedCrossRefGoogle Scholar
  31. 31.
    Kraan MC, Haringman JJ, Ahern MJ, Breedveld FC, Smith MD, Tak PP. Quantification of the cell infiltrate in synovial tissue by digital image analysis. Rheumatology. 2000;39(1):43–9.PubMedCrossRefGoogle Scholar
  32. 32.
    Noutsias M, Pauschinger M, Ostermann K, Escher F, Blohm JH, Schultheiss H, et al. Digital image analysis system for the quantification of infiltrates and cell adhesion molecules in inflammatory cardiomyopathy. Med Sci Monit. 2002;8(5):MT59-71.PubMedGoogle Scholar
  33. 33.
    Sont JK, De Boer WI, van Schadewijk WA, Grunberg K, van Krieken JH, Hiemstra PS, et al. Fully automated assessment of inflammatory cell counts and cytokine expression in bronchial tissue. American journal of respiratory and critical care medicine. 2003;167(11):1496–503.PubMedCrossRefGoogle Scholar
  34. 34.
    Ekeblad S, Skogseid B, Dunder K, Oberg K, Eriksson B. Prognostic factors and survival in 324 patients with pancreatic endocrine tumor treated at a single institution. Clinical cancer research : an official journal of the American Association for Cancer Research. 2008;14(23):7798–803.CrossRefGoogle Scholar
  35. 35.
    Fischer L, Kleeff J, Esposito I, Hinz U, Zimmermann A, Friess H, et al. Clinical outcome and long-term survival in 118 consecutive patients with neuroendocrine tumours of the pancreas. Br J Surg. 2008;95(5):627–35.PubMedCrossRefGoogle Scholar
  36. 36.
    Pape UF, Jann H, Muller-Nordhorn J, Bockelbrink A, Berndt U, Willich SN, et al. Prognostic relevance of a novel TNM classification system for upper gastroenteropancreatic neuroendocrine tumors. Cancer. 2008;113(2):256–65.PubMedCrossRefGoogle Scholar
  37. 37.
    Garcia-Carbonero R, Capdevila J, Crespo-Herrero G, Diaz-Perez JA, Martinez Del Prado MP, Alonso Orduna V, et al. Incidence, patterns of care and prognostic factors for outcome of gastroenteropancreatic neuroendocrine tumors (GEP-NETs): results from the National Cancer Registry of Spain (RGETNE). Ann Oncol. 2010;21(9):1794–803.PubMedCrossRefGoogle Scholar
  38. 38.
    Scarpa A, Mantovani W, Capelli P, Beghelli S, Boninsegna L, Bettini R, et al. Pancreatic endocrine tumors: improved TNM staging and histopathological grading permit a clinically efficient prognostic stratification of patients. Mod Pathol. 2010;23(6):824–33.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Trynda N. Kroneman
    • 1
  • Jesse S. Voss
    • 1
  • Christine M. Lohse
    • 2
  • Tsung-Teh Wu
    • 1
  • Thomas C. Smyrk
    • 1
  • Lizhi Zhang
    • 1
    Email author
  1. 1.Division of Anatomic Pathology, Department of Laboratory Medicine and PathologyMayo ClinicRochesterUSA
  2. 2.Division of Biomedical Statistics and InformaticsMayo ClinicRochesterUSA

Personalised recommendations