Large B Cell Lymphoma

Diffuse Large B-cell Lymphoma (DLBCL) research focuses on improving diagnosis and predicting treatment response, ultimately aiming to personalize patient care and improve outcomes. Current efforts leverage advanced machine learning techniques, including deep learning models like cascaded networks and vision transformers, to analyze histopathology images (H&E stained tissue and Whole Slide Images) and PET scans, extracting features for improved subtype classification and response prediction. These computational approaches are showing promise in automating time-consuming tasks, enhancing diagnostic accuracy compared to traditional methods, and potentially identifying patients at higher risk of treatment failure. The development of interpretable AI models is also crucial for building trust and understanding the underlying biological mechanisms driving DLBCL.