Publications by authors named "Nilay Bakoglu"
Article Synopsis
- AI technology in pathology relies on supervised machine learning and is increasingly used for tasks like detecting invasive breast cancer and analyzing tissue layers in colorectal specimens.
- The study involved training convolutional neural networks (CNNs) using a large dataset of whole-slide images (WSIs) across different cancer types, achieving notable F1 scores for diagnostic accuracy.
- After extensive training and external validation, the AI models demonstrated a high level of proficiency in identifying tumors and distinguishing between various tissue layers.
() gene amplification and subsequent protein overexpression is a strong prognostic and predictive biomarker in invasive breast carcinoma (IBC). ASCO/CAP recommended tests for HER2 assessment include immunohistochemistry (IHC) and/or hybridization (ISH). Accurate HER2 IHC scoring (0, 1+, 2+, 3+) is key for appropriate classification and treatment of IBC.
View Article and Find Full Text PDFIn colorectal carcinoma (CRC), tumor deposits (TDs) are described as macroscopic/microscopic nests/nodules in the lymph drainage area discontinuous with the primary mass, without identifiable lymph node (LN) tissue, and not confined to vascular or perineural spaces. A TD is categorized as pN1C only when no bona fide LN metastasis exists. However, there has been an ongoing debate on whether TDs should be counted as LNs.
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