Artificial Intelligence Role in Subclassifying Cytology of Thyroid Follicular Neoplasm.

Objective: Fine needle aspiration cytology has higher sensitivity and predictive value for diagnosis of thyroid nodules than any other single diagnostic methods.  In the Bethesda system for reporting thyroid, the category IV, encompasses both adenoma and carcinoma, but it is not possible to differentiate both lesions in the cytology practice and can be only differentiated after resection. In this work, we aim at exploring the ability of a convolutional neural network (CNN) model to sub-classifying cytological images of Bethesda category IV diagnosis into follicular adenoma and follicular carcinoma.

Expression of PD-L1 in Locally Advanced Breast Cancer and Its Impact on Neoadjuvant Chemotherapy Response.

Objectives: Programmed cell death-ligand 1 (PD-L1) is a new target in breast cancer (BC) and its impact on neoadjuvant chemotherapy (NACTH) response is still unclear. The aim of this study was to investigate the prevalence of PD-L1 in locally advanced invasive BC of different molecular subtypes and to elucidate its relation to tumor-infiltrating lymphocytes (TILs) density, established clinicopathological factors, pathological therapy response after neoadjuvant chemotherapy and patients' outcome.

Materials And Methods: One hundred and five cases of locally advanced invasive BC were enrolled in our study.