Metastasis is the process by which cancer spreads from the place at which it first arose as a primary tumor to distant locations in the body.

The study reveals a hidden connection between the tumor and the nutrient supplying vessels. The method outlines paths of future tumor expansion and identifies specific points in the vessels that can be targeted to control the growth, explains Neil Johnson, professor of physics, director of the Complexity Research Group at UM College of Arts and Sciences and co-principal investigator of the study.

Neil Johnson said: “Cancer is a disease of many scales. There are the individual cells, the cells that group together to form the tumor, the vasculature and finally metastasis. By including information about how the tumor grows in response to its nutrients, and how the growth of the tumor feeds back the nutrient supply itself, our model moves us one step closer to predicting the future evolution of a patient's tumor. It opens up a path toward personalized treatment and intervention.”

One interesting aspect of the model is that it's based on the distribution of feeding vessels in a tumor section. Since the vessels both feed and are fed by the tumor, estimates of growth characteristics for a patient's tumor can be made.

This type of estimate can potentially be applied to a better design of treatment schedules for cancer patients, explains Joseph Rosenblatt, interim director of Sylvester Comprehensive Cancer Center, at UM Miller School of Medicine and co-principal investigator of the study.

By analyzing images of tumor sections for distribution of tumor cells and tumor vasculature, the researchers created a simple model that predicts the most likely course of the disease, explains Sehyo Choe, post-doctoral research fellow at the Division of Theoretical Bioinformatics at the German Cancer Research Center and co-principal investigator of the study.

Sehyo Choe said: "Our model implements local differences of a tumor directly extracted from in vivo images, and the parameters are directly measurable for each cancer. By doing so, we believe we are one step closer to eventually building a model that will be able to describe a likely corridor of progression of a cancer, based on real-time information of a specific patient from images and other patient specific data."

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