MET Overexpression

MET overexpression in non-small cell lung cancer (NSCLC) is a significant driver of tumor growth and a key therapeutic target, but current detection methods are limited by cost and tissue consumption. Research focuses on developing cost-effective predictive models using readily available hematoxylin and eosin (H&E) stained slides, leveraging machine learning algorithms like logistic regression and weakly supervised models to analyze image features such as cell morphology, color, and texture. Successful models demonstrate moderate accuracy in predicting MET alterations from H&E images, potentially streamlining patient selection for targeted therapies and improving access to precision oncology.