Intra Tumoral Genetic Heterogeneity

Intra-tumoral genetic heterogeneity, the variability in genetic makeup within a single tumor, significantly impacts cancer diagnosis, treatment, and prognosis. Current research focuses on developing non-invasive methods, primarily using machine learning algorithms like support vector machines and deep learning architectures (including neural ODEs and vision transformers), to quantify this heterogeneity from medical images (MRI, H&E stained histology) and multi-modal data. These efforts aim to improve the accuracy of predicting patient survival and response to therapy, ultimately enabling more personalized and effective cancer treatment strategies. The ability to non-invasively assess this heterogeneity holds substantial promise for advancing precision oncology.