Artificial Intelligence (AI) - Based Models for Biomarkers Prediction

Despite the improvements in therapeutic approaches, breast cancer remains the most commonly occurring cancer and leading cause of cancer death in women worldwide. Breast cancer treatment decisions are based mainly on the presence or absence of established prognostic and predictive biomarkers, mainly the hormone receptors, estrogen (ER) and progesterone (PgR), the human epidermal growth factor receptor 2 oncoprotein (HER2) in correlation with ki67 proliferation index. Currently, PCR-based assays, next-generation sequencing (NGS), and immunohistochemistry (IHC), followed by in situ hybridization (ISH) when needed, are used for biomarker testing. Ιn an effort to overpass technical limitations, human subjectivity which may yield different results between different observers leading to inappropriate treatment and complications that can adversely affect patient quality of life, alternative, cost-effective approaches using AI-based digital pathology analysis of histopathology images has received significant attention last years. Digital pathology uses whole-slide image scanners and combined with artificial intelligence (AI) algorithms intend to improve diagnostic accuracy, enabling rapid patient stratification and optimal treatment regimens, thereby improving patient outcomes. Identification of invasive breast tumors and lymph node metastasis, evaluation of hormonal status, breast cancer grading and mitotic count evaluation, biomarkers identification based on images of the tumor microenvironment have also been enhanced by AI quantitative analysis. AI has promising results so far. More studies are, however, needed in order to overcome obstacles and before it is validated for patient care in the clinical setting. DL technology has been implemented in clinical pathology in an attempt to automate the way pathologists evaluate hematoxylin and eosin (H/E) stained slides, but also to attain information such as molecular background and protein expression, given that genetic vari