Background: Fine-needle aspiration cytology (FNAC) has become a well-established modality in the diagnosis, staging and follow-up of thyroid nodules. FNAC outcomes are routinely classified using the Bethesda System for Reporting Thyroid Cytopathology (BSRTC), facilitating appropriate clinical management. Bethesda categories III and IV encompass varying risks of malignancy. This retrospective study established a possible association between these cytological categories and malignancy rates in patients treated at a single institution. Methods: Over a 6-year period, 11,627 FNAC procedures were performed on thyroid nodules. Of these, 814 (59.63%) patients were submitted to thyroidectomy. The nodules of 108 patients were classified as Bethesda category III and 47 patients as Bethesda category IV. Patient data were reviewed to establish a correlation between the FNAC results and the final histopathological analyses. Results: The rates of malignancy among patients who underwent surgery were 25% for category III and 27.6% for category IV, with no significant differences between categories (p = 0.67). The pathological parameters of malignant nodules, namely tumour type, size, encapsulation, invasion into the thyroid capsule, extrathyroidal extension and lymphovascular invasion did not significantly differ between the groups (p > 0.05). Conclusions: This paper provides a more precise correlation of malignancy rates with thyroid nodules classified as Bethesda categories III and IV, as our findings are comparable to the literature, giving malignancy rates ranging from 10 to 30% for category III and 25–40% for category IV. Use of the BSRTC is heterogeneous across institutions, and there is some degree of subjectivity in the distinction between categories III and IV; therefore, it is crucial to estimate the rates of malignancy at each institution. Molecular assays are of increasing importance in determining the need for surgical intervention for thyroid lesions. Gene expression assays using FNAC material may demonstrate a high predictive value for cytologically indeterminate thyroid nodules diagnosed as Bethesda classes III and IV.

To evaluate the effectiveness of treatment with topical and intralesional steroids for idiopathic granulomatous mastitis (IGM) and to compare with surgical methods.Data were retrospectively collect...

Joint Diseases and Related Surgery (JDRS), the official journal of the Turkish Joint Diseases Foundation, is an international, peer-reviewed, immediate open access journal. All published articles in JDRS has been free for everyone, without embargo, since 1990.

Prostate cancer is a highly prevalent tumor among men and probably histologically represents one of the most common malignant tumors in the world. The automated analysis of tissues in histopathology has been advancing with higher accuracy rates using modern slide imaging techniques in combination with deep learning approaches. Today, pathologists can examine tissue samples in detail on high-resolution digital images thanks to whole slide imaging (WSI) images. This helps to accurately diagnose and evaluate the grades of prostate cancer. Tasks for analyzing WSI images with deep learning methods rely heavily on WSI images where tumor regions are marked by pathologists. This poses the challenge of marking WSI images for pathologists. In this study, we developed deep learning-based models with transfer learning support to classify prostate cancer grade in WSIs based on slide-level labels without the need for pathologists to mark the images. For this aim, we collected a real-life dataset which contains WSI image data of prostate cancer patients obtained from Kocaeli University, Department of Medical Pathology. The collected WSI data were labeled with prostate cancer grades only which are slide-level annotations, without the identification of regions of interest (ROIs) such as tumor regions within the tissue section. To demonstrate the contribution of the proposed, WSI-based classification models, they are compared with patch-based models regarding accuracy, precision, recall, and F1 score. Experimental results show that the proposed model provides high classification accuracy for prostate cancer detection over WSI images.

Objective: The basis for current and future lung cancer immunotherapy depends on our knowledge of molecular mechanisms of interactions between tumor and immune system cells. Interactions that occur between different intratumoral populations of the same cells are important. In our study, we aimed to evaluate relationship between the clinical and prognostic features and T lymphocyte subgroups of patients with lung tumors after neoadjuvant treatment. Methods: A total of 72 patients were included in our study, including study group, 39 of whom received neoadjuvant chemotherapy. Clinical/radiological/pathological findings of patients and CD4/CD8 staining rates in peritumoral/intratumoral areas were recorded. Results: Our study revealed significantly lower intratumoral CD4 + T cell density and lower intratumoral CD4/CD8 ratio in primary tumor after neoadjuvant therapy (respectively, 0.012 and 0.016). Considering tumor types, when control-study groups were compared, inflammation was statistically significant only in adenocarcinoma subtype; intratumoral CD4/CD8 ratio was statistically significant only in squamous-cell carcinoma subtype (respectively, p = 0.0008 and p = 0.0139). When CD4 + T lymphocytes and CD8 + T lymphocytes and CD4/CD8 ratio were compared between control and study groups in low-stage patients according to clinical stages, only intratumoral CD4 + T lymphocyte values and intratumoral CD4/CD8 ratio were significant (respectively, p = 0.0291 ve p = 0.0154). Conclusion: All cell types of innate and adaptive intratumoral immunity can affect lung cancer tissues simultaneously, and these interactions have a very complex structure. Understanding the tumor microenvironment and the different roles of associated cancer immune cells may lead to the discovery of new targets for immunological therapies and increased survival times in lung cancer.