Digital Methods for Breast Cancer Detection and Distinguishing Different Lesions

Prof. Bhargava has developed a digital method for identifying areas of early stage tumor and distinguishing them from benign population in breast cancer lesions. This highly accurate artificial intelligence method can assist pathologists to interpret biopsies in breast cancer screening, increasing their efficiency and accuracy. About a million breast biopsies are performed in the US, out of which quarter receive a cancer diagnosis. The remaining patient cases are categorized by pathologists according to a diagnostic spectrum ranging from benign to preinvasive diseases which are difficult to classify. Misclassification can contribute to over treatment or undertreatment of lesions causing serious health and financial repercussions. Precise stratification of the lesions will reduce under or over diagnosis and time in breast cancer diagnosis.

This digital toolbox will be of interest to both health care clinics for risk stratification and early detection and digital pathology companies as a translatable diagnostic suite.