Timik karsinoid tümörler nadir tümörler olup, multipl endokrin neoplazi tip 1 ile birlikte görülebilirler.Bronşiyal karsinoidler de nadir tümörler olup, multipl endokrin neoplazi tip 1'e eşlik edebilirler.Bu durumda timik ve bronşiyal karsinoid tümörlerin
Prior corticosteroid therapy presents a major challenge in the diagnosis of CNS lymphomas, particularly in stereotactic biopsies. In this study we analysed the cytological, histopathological and immunohistochemical features in stereotactic biopsies of 25 primary CNS lymphoma cases pre-treated with corticosteroids. We documented the extent and the frequency of each finding. We also investigated the significance of subjectivity in evaluation of these biopsies in 3 seperate sessions including the final diagnostic decision. In 48% of our cases the diagnosis was straightforward. These cases were characterized by prominent blasts either in diffuse paranchymal infiltrates or in perivascular regions. The remaining 52% demonstrated some degree of variability among pathologists. Lymphoid atypia other than the typical blastic morphology appeared as a subjective finding and this was more pronounced in cytology preparations. In our study, corticosteroid pre-treatment in primary CNS lymphoma was associated with a large spectrum of histopathological, immunohistochemical and cytological findings. Combined use of an extended immunohistochemical panel would increase the possibility of conclusive diagnosis. Nevertheless some of these findings and therefore the diagnosis are open to subjectivity.
Urine cytology remains to be the test of choice in the detection of high-grade urothelial carcinomas (HGUC) due to its favorable sensitivity. However, a significant rate of cases is reported under atypical/indeterminate categories, which result in a decrease in its specificity. Providing standardized cytologic criteria, one of the aims of The Paris System (TPS) is to reduce the use of indeterminate diagnoses and provide a higher predictive value in these categories.We compared the diagnostic performances of TPS and our original reporting system, and also investigated the interobserver reproducibility of the cytologic criteria used.A total of 386 urine samples were reviewed retrospectively. Original cytologic diagnoses have been made using similar cytologic features proposed by TPS. All slides were recategorized after the use of the cytologic criteria as described by TPS guideline.After TPS, specificity of the test increased from 39.6% to 63.5, sensitivity decreased from 92.5% to 88.8%, and diagnostic accuracy increased from 63.6% to 75%. The use of negative category increased threefold. Frequencies of indeterminate categories of atypical urothelial cells (AUC) and suspicious for HGUC (SHGUC) decreased by 36% and 56.5%, respectively. A subsequent detection of HGUC after AUC and SHGUC categories increased by 38% and 64%, respectively. Interobserver agreement for TPS categorization was 39%.TPS improved diagnostic accuracy of urine cytology by reducing the use of indeterminate categories, and resulted in increase in their predictive value for subsequent diagnosis of HGUC. However, reproducibility of diagnostic categories seemed to be imperfect.
Deep learning-based approaches have shown highly successful performance in the categorization of digitized biopsy samples. The commonly used setting in these approaches is to employ convolutional neural networks for classification of data sets consisting of images all having the same size. However, the clinical practice in breast histopathology necessitates multi-class categorization of regions of interest (ROI) in biopsy samples where these regions can have arbitrary shapes and sizes. The typical solution to this problem is to aggregate the classification results of fixed-sized patches cropped from these images to obtain image-level classification scores. Another limitation of these approaches is the independent processing of individual patches where the rich contextual information in the complex tissue structures has not yet been sufficiently exploited. We propose a generic methodology to incorporate local inter-patch context through a graph convolution network (GCN) that admits a graph-based ROI representation. The proposed GCN model aims to propagate information over neighboring patches in a progressive manner towards classifying the whole ROI into a diagnostic class. The experiments using a challenging data set for a 4-class ROI-level classification task and comparisons with several baseline approaches show that the proposed model that incorporates the spatial context by using graph convolutional layers performs better than commonly used fusion rules.
