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.

Xanthogranulomatous prostatitis as mimicker of prostate adenocarcinoma can cause a diagnostic dilemma, as presented in this case. Therefore, alongside histopathology analysis, multiparametric magnetic resonance imaging (mpMRI) would be useful in this situation by identifying and characterizing suspicious prostatic lesions before biopsy thereby supporting current recommendations on the use of mpMRI.

<p>Hypereosinophilia is a rare paraneoplastic nding in malignant disease, particularly lung cancer. When it occurs, it is usually indicative of<br />metastatic disease. We describe a 52-year-old male patient with paraneoplastic hypereosinophilia associated with primary adenocarcinoma<br />of the right lower lobe and extensive metastatic disease.</p>

The findings of this study highlight the need for allocation of diagnostic and treatment resources for KS. Documentation of the various demographic aspects of KS will prove to be of historical, clinical and histopathological interest as the long-term outcomes of antiretroviral therapy begin to emerge.

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The histologic diagnosis of Kaposi sarcoma (KS) can be confirmed with human herpes virus 8 (HHV8) latency-associated nuclear antigen (LNA)-1 immunohistochemistry, which may show variability in distribution and intensity. This retrospective study was aimed at addressing the factors that may contribute to this variability. All cases of mucocutaneous KS diagnosed in a 5-year period at the histopathology department at a tertiary hospital in South Africa with available patients' CD4 counts and HHV8 LNA-1 immunohistochemically stained slides were reviewed, and the biopsy stages of KS (patch/plaque/nodular), CD4 counts, immunohistochemistry staining method (manual vs. automated), and distribution (diffuse/focal) and intensity (strong/weak) of HHV8 LNA-1 staining were recorded. A total of 127 cases were reviewed. No relationship was demonstrated between the median CD4 count and the histologic stages of KS (P = 0.701) or the intensity and distribution of HHV8 immunohistochemical staining using either staining method. Multivariate analysis showed that method of immunohistochemical staining was a significant predictor of distribution (P = 0.006) and intensity (P = 0.044) of staining, and that stage was a significant predictor of distribution of staining (P = 0.033).

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