May 22, 2019 | Updated: 08:18 AM EDT

Predicting Tumor Growth Through Math

Dec 14, 2015 04:18 AM EST

Mathematicians want to create a model that can calculate tumor size to enhance health professionals' treatment regimen
(Photo : Getty Image)

Statistics reveals that one out of three people in the United States is diagnosed with cancer. Looking for certain formulas to predict tumor growth has been an interest of cancer research. Recently, mathematicians from Duke University suggest that correct mathematical calculations of the actual tumor growth might help healthcare practitioners deal with this deadly disease.

Researchers propose that the key to finding out the correct way of treating cancer, such as determining accurate therapy dose and dosages, screening procedure evaluation, enhancing radiation protocol and making medically inclined decisions for patient treatment, is figuring out the precise tumor growth. 

There have been a number of proposed research in line with this assumption. However, determining which model is applicable to the number of types of cancer is not known yet. "Some tumors stop or slow down once they reach a certain size, while others continue to grow," Anne Talkington, study co-author, stated.

One of the challenges these models face is that most of these are measured only in tumors growing in mice or in the laboratory. This can pose certain differences to the actual dynamics of the human body, where, for instance, oxygen supply and nutrients vary. Researchers though admit that these growth data are difficult to obtain as most of the patients diagnose with cancer immediately undergo therapy or surgery.

In their paper presented in the Bulletin of Mathematical Biology, the mathematicians explained a way to compare these models of tumor growth through two time-point measurement. These are the baseline data that are usually the maximum number of size obtained from patients before therapy.

They tested whether their proposed theory works by consulting previous research. They found five studies that utilized two time points acquired via repeat tests of mammograms, CT scans and MRIs. 

The results revealed that tumors found in the liver and breast increasingly grow. Durrett compared the scenario to a bank account with interest constantly and permanently growing. In addition, there are two kinds of brain tumors that grow the two-thirds power of law. This is incongruence with to the idea that only cells found in the surface are able to multiply.

"Some bias has been introduced by the way the data were obtained, but our results indicate that the method is useful for determining which tumor growth models works best for different types of cancer," Talkington said.

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