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. METHODS: We used a cohort of cytology cases n= 43 with extracted images n= 886 to train CNN model aiming to sub-classify follicular neoplasm (Bethesda category IV) into either follicular adenoma or follicular carcinoma. RESULT: In our study, the model subclassification of follicular neoplasm into follicular adenoma (n = 28/43, images n = 527/886) from follicular carcinoma (n = 15/43, images n= 359/886), has achieved an accuracy of 78%, with a sensitivity of 88.4%, and a specificity of 64% and an area under the curve (AUC) score of 0.87 for each of follicular adenoma and follicular carcinoma. CONCLUSION: Our CNN model has achieved high sensitivity in recognizing follicular adenoma amongest cytology smears of follciualr neoplasms, thus it can be used as an ancillary technique in the subcalssification of Bethesda Iv category cytology smears.

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. Cases were classified into five molecular subtypes according to the Immuno-histochemical data. PD-L1 immunostaining was analyzed for all studied cases and its expression was correlated with TILs density, histopathologic parameters, BC molecular subtypes, Pathological therapy response, 7-years disease-free survival (DFS) and overall survival (OS). RESULTS: PD-L1 was expressed in 32.4% of the studied locally advanced BC cases. It showed a significant correlation with old age group (p= 0.010), high tumor grade (p= 0.046) and high pretherapy TILs density (p= <0.001). PD-L1 expression was higher in HER2/neu-enriched group (45.5%) followed by TNBC (44.4%). There were no significant relations between PD-L1 expression and DFS, OS as well as pathological therapy response, although, it revealed more expression in cases with complete and marked therapy response. CONCLUSION: In spite our results fail to prove that PD-L1 is a bad prognostic biomarker in locally advanced BC, but they indicate PD-L1 could be a new target for the treatment of patients with high grade breast carcinoma and TNBC group.