Quantifying dynamics of cancer resistance using advanced image processing and statistical models.

Resistance to therapies is a significant challenge in successful treatment of cancers. Frequently, this resistance arises from subtle genetic variations within individual malignant cells that evade therapy and subsequently multiply. In this endeavor, we aim to employ image processing tools to track the dynamics of resilient clones using microscopy images provided by our collaborator at Northwestern University. Our ultimate goal is to classify the dynamics of the clones using advanced statistical tools and build a computational model to predict for scenarios beyond experimental access.