Modeling will help researchers predict where cancer might spread in patients, better screen them

Lung cancer can be a particularly deadly one as it can quickly spread to some parts of the body once it metastasizes (once tumor cells enter the bloodstream).  But the process of metastasis is poorly understood.

Looking to model the process researched at the University of Southern California emulated the underlying mechanics of Google Inc.'s (GOOGPage Rank algorithm and the Viterbi Algorithm (another popular algorithm used in GSM cell phone networks using probabilistic Markov chain-driven convolution) to model the spread of the cancer.

They found that the adrenal gland and kidney act as lung cancer cell "spreaders" distributing tumor cells around the body.  By contrast, when infiltrated by tumor cells the regional lymph nodes, liver, and bones acted as "sponges" and did not significantly spread around the disease.

To get data for the model, the researchers used autopsy information on essentially untreated patients.  They used data from 163 lung cancer patients in the New England area, from 1914 to 1943.  In this era effective radiation therapies and chemotherapies did not exist, so the disease essentially spread unchecked.

Lung cancer
Researchers have used Google's PageRank algorithm to help model the seemingly unpredictable spread of metastatsized lung cancer cells. [Image Source: SPL]

There's growing interest in the medical field to use mathematical models to predict the spread of cancer.  By using past data, doctors treating a particular kind of cancer can keep their eye out for where it might spread -- and which locations it would be most dangerous for it to spread to.

USC School of Engineering Professor Paul Newton, Ph.D, comments, "This research [using PageRank to model cancer] demonstrates how similar the Internet is to a living organism.  The same types of tools that help us understand the spread of information through the web can help us understand the spread of cancer through the human body."

The study on the work has been published [abstract] in the peer-reviewed journal Cancer Research.  It was funded by the National Cancer Institute and the Gates Millennium Fellowship Program of the Bill and Melinda Gates Foundation.

Sources: Cancer Research [abstract], USC [press release]

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