Eunice J van den Berg1,2, Raquel Duarte3, Caroline Dickens3, Maureen Joffe4, Reena Mohanlal1,2. 1. Department of Histopathology, National Health Laboratory Service. 2. Departments of Anatomical Pathology. 3. Internal Medicine, University of the Witwatersrand. 4. Chris Hani Baragwanath Academic Hospital Breast Clinic, Johannesburg, South Africa.
Abstract
BACKGROUND: Molecular analysis has shown that breast carcinomas can be classified into several intrinsic subtypes, with implications for management and prognosis. In the majority of pathology laboratories molecular analysis of each case is not possible and immunohistochemistry is used for subtyping. This includes analysis of hormone receptors as well as HER2-neu and Ki67. The methodology for the interpretation of the proliferation index using Ki67 remains an area of uncertainty. We investigated the degree of agreement between different methods of Ki67 interpretation. MATERIALS AND METHODS: We analyzed 204 breast core biopsies diagnostic of breast carcinoma using visual estimation/eyeballing (EB), ImmunoRatio, and counting by 2 pathologists (CP1 and CP2). The correlation between the different methods and the interobserver agreement between the 2 pathologists was assessed. Specific analysis was also done with respect to classification of cases into low Ki67 groups (using Ki67 values<14% and <20%) since this is critical in classifying tumors into luminal A and luminal B subtypes. RESULTS: Correlation between the different methods was best achieved comparing ImmunoRatio and CP1, and worst comparing CP1 and EB. Correlation was better when considering interobserver variability (CP1 vs. CP2). Comparing the number of cases classified as low Ki67 (<14% and <20%) the Cohen κ statistic varied from κ=0.267 to 0.814 with different methods. When limiting the analysis to cases with a Ki67 of 10% to 25% according to any method, there was greater disagreement. CONCLUSIONS: At the higher and lower Ki67 levels, the correlation between the methods of assessment was acceptable, however, at levels close to the cut-off values for lumial A versus luminal B, several patients would be differently classified by the different methods and therefore potentially receive suboptimal management.
BACKGROUND: Molecular analysis has shown that breast carcinomas can be classified into several intrinsic subtypes, with implications for management and prognosis. In the majority of pathology laboratories molecular analysis of each case is not possible and immunohistochemistry is used for subtyping. This includes analysis of hormone receptors as well as HER2-neu and Ki67. The methodology for the interpretation of the proliferation index using Ki67 remains an area of uncertainty. We investigated the degree of agreement between different methods of Ki67 interpretation. MATERIALS AND METHODS: We analyzed 204 breast core biopsies diagnostic of breast carcinoma using visual estimation/eyeballing (EB), ImmunoRatio, and counting by 2 pathologists (CP1 and CP2). The correlation between the different methods and the interobserver agreement between the 2 pathologists was assessed. Specific analysis was also done with respect to classification of cases into low Ki67 groups (using Ki67 values<14% and <20%) since this is critical in classifying tumors into luminal A and luminal B subtypes. RESULTS: Correlation between the different methods was best achieved comparing ImmunoRatio and CP1, and worst comparing CP1 and EB. Correlation was better when considering interobserver variability (CP1 vs. CP2). Comparing the number of cases classified as low Ki67 (<14% and <20%) the Cohen κ statistic varied from κ=0.267 to 0.814 with different methods. When limiting the analysis to cases with a Ki67 of 10% to 25% according to any method, there was greater disagreement. CONCLUSIONS: At the higher and lower Ki67 levels, the correlation between the methods of assessment was acceptable, however, at levels close to the cut-off values for lumial A versus luminal B, several patients would be differently classified by the different methods and therefore potentially receive suboptimal management.
Authors: A S Coates; E P Winer; A Goldhirsch; R D Gelber; M Gnant; M Piccart-Gebhart; B Thürlimann; H-J Senn Journal: Ann Oncol Date: 2015-05-04 Impact factor: 32.976
Authors: T Sørlie; C M Perou; R Tibshirani; T Aas; S Geisler; H Johnsen; T Hastie; M B Eisen; M van de Rijn; S S Jeffrey; T Thorsen; H Quist; J C Matese; P O Brown; D Botstein; P E Lønning; A L Børresen-Dale Journal: Proc Natl Acad Sci U S A Date: 2001-09-11 Impact factor: 11.205
Authors: C Denkert; S Loibl; B M Müller; H Eidtmann; W D Schmitt; W Eiermann; B Gerber; H Tesch; J Hilfrich; J Huober; T Fehm; J Barinoff; C Jackisch; J Prinzler; T Rüdiger; E Erbstösser; J U Blohmer; J Budczies; K M Mehta; G von Minckwitz Journal: Ann Oncol Date: 2013-08-22 Impact factor: 32.976
Authors: N Bouzubar; K J Walker; K Griffiths; I O Ellis; C W Elston; J F Robertson; R W Blamey; R I Nicholson Journal: Br J Cancer Date: 1989-06 Impact factor: 7.640
Authors: A Goldhirsch; E P Winer; A S Coates; R D Gelber; M Piccart-Gebhart; B Thürlimann; H-J Senn Journal: Ann Oncol Date: 2013-08-04 Impact factor: 32.976