Ki-67 cytological index can distinguish well-differentiated from poorly differentiated pancreatic neuroendocrine tumors: a comparative cytohistological study of 53 cases
- PMID: 24807732
- DOI: 10.1007/s00428-014-1585-7
Ki-67 cytological index can distinguish well-differentiated from poorly differentiated pancreatic neuroendocrine tumors: a comparative cytohistological study of 53 cases
Abstract
The Ki-67 labeling index has been found to bear prognostic significance in gastrointestinal neuroendocrine tumors (NETs), and it was recently incorporated in NET histological grading. Nevertheless, a reliable preoperative determination of NET grading could be useful in clinical practice. The aim of this study is to compare the results of Ki-67 labeling index, as measured on cytological samples and on surgical specimens of patients with pancreatic NETs (P-NETs). We also investigated whether concordance might be improved, using a 5 % (instead of 2 %) cutoff value for defining G2 tumors. We retrospectively identified 48 consecutive patients with 53 P-NETs, from our five institutions, and we measured Ki-67 labeling index on their cytological samples and surgical specimens. The traditional 2 % and the alternative 5 % cutoff values were used to classify G2 tumors. The concordance rate between cytological and histological grading was 46/53 (86.8 %; weighted κ statistic 0.77; 95 % confidence interval (95 % CI) 0.60-0.94). No cases of cytological G1-G2 NETs were upgraded to G3 neuroendocrine carcinoma (NEC) at histological grading. Cytology was found to be highly specific in the diagnosis of both G2 (94.1 %; 95 % CI 80.3-99.3) and G3 tumors (100.0 %; 95 % CI 92.8-100), but the sensitivity was poor for G2 NETs (66.7 %; 95 % CI 38.4-88.2) and high for the prediction of G3 NECs (100 %; 95 % CI 39.8-100.0). When the 5 % cutoff value was adopted, concordance rate was 49/53 (92.4 %; weighted κ 0.82; 95 % CI 0.64-1.00). In conclusion, Ki-67 cytological expression can distinguish well-differentiated (both G1 and G2) from poorly differentiated P-NETs, and it may be useful for their preoperative classification.
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