Tumor Burden

Tumor burden assessment, crucial for evaluating cancer treatment response and prognosis, is undergoing a transformation driven by advancements in image analysis and deep learning. Current research focuses on developing automated methods, such as self-supervised image registration and deep learning pipelines, to accurately quantify tumor volume and heterogeneity from medical images like CT and MRI scans, improving upon traditional methods and reducing inter-observer variability. These advancements are improving the precision of treatment monitoring and potentially identifying new biomarkers, like tumor mutational burden (TMB), directly from pathology images, thereby enhancing personalized cancer care.