Clinical validation of the gastrointestinal NET grading system: Ki67 index criteria of the WHO 2010 classification is appropriate to predict metastasis or recurrence

Abstract

Background: In the WHO 2010 classification, the neuroendocrine tumors (NETs) are subdivided by their mitotic index or Ki67 index into either G1 or G2 NETs. Tumors with a Ki67 index of <2% are classified as G1 and those with 3-20% are classified as G2. However, the assessment of tumors with Ki67 index of greater than 2% and less than or equal to 3% is still unclear. To resolve the problem, we validated the Ki67 index criteria of gastrointestinal NETs of the WHO 2010 classification.

Methods: The medical records of 45 patients who were pathologically diagnosed as having NET G1/G2 of the gastrointestinal tract were analyzed retrospectively. According to the WHO 2010 classification, Ki67 index were calculated. Computer-assisted cytometrical analysis of Ki67 immunoreactivity was performed using the WinRooF image processing software. Receiver operating characteristic (ROC) curves were generated to determine the best discriminating Ki67 index. To clarify the assessment of tumors with Ki67 index between 2-3%, the calculated cutoff of Ki67 index was evaluated using Fisher's exact test.

Results: ROC curve analysis confirmed that 2.8% was the best Ki67 index cutoff value for predicting metastasis or recurrence. The sensitivity of the new Ki67 index cutoff was 42.9%, and the specificity was 86.8%.

Conclusions: Division of NETs into G1/G2 based on Ki67 index of 3% was appropriate to predict metastases or recurrences. The WHO grading system may be the most useful classification to predict metastases or recurrences.

Virtual slides: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1553036118943799.

Figure 1

Images of a case of NET. This case is rectal NET with multiple liver and lung metastasis, and its Ki67 index is 2.8%. A: Hematoxylin and eosin staining. B: Immunohistochemical findings for Ki-67. C: Image of WinROOF. Count the Ki67 positive cells by B (blue) image of enhanced contrast for RGB color separation. D: Image of WinROOF. Count all tumor cells by R (red) image of enhanced contrast for RGB color separation.

Figure 2

The ROC curve analysis to determine the best discriminating Ki67 index. Sens: sensitivity, Spec: specificity, PV+: positive predictive value,PV-: negative predictive value.