Using highly sophisticated computer programs that mimic human intelligence, researchers at the University of Maryland Greenebaum Cancer Center in Baltimore have devised a new method to differentiate and diagnose several types of colon tumors.
The method, which uses "artificial neural networks," or ANNs, to analyze thousands of genes at one time, could ultimately help doctors to identify the cancers earlier and spare some patients from unnecessary, debilitating surgery, says Stephen J. Meltzer, M.D., professor of medicine at the University of Maryland School of Medicine. Dr. Meltzer is the senior author of a study to be featured on the cover of the March issue of Gastroenterology, the journal of the American Gastroenterological Association.
Patients with Crohns disease and ulcerative colitis, the two forms of inflammatory bowel disease (IBD), have an increased risk of developing cancer, but the cancer can be one of two forms. "Sporadic," or common, colon cancers can often be removed without radical surgery, while IBD-related growths and cancers are much more aggressive and are generally treated by taking out the entire colon.
Until now, we had no reliable way to discriminate between these two types of lesions, especially in their early stages," says Dr. Meltzer, who is also associate director for core sciences at the University of Maryland Greenebaum Cancer Center and director of the cancer centers Genomics Core Facility.
"This study helps to establish a new method, called artificial neural networks (ANNs), that can be used in a wide variety of disease settings, not just in cancer," he says. "These networks mimic the human brain, in that they can be trained to recognize specific disease lesions or subtle differences within disease categories. Ultimately, we hope that ANNs will greatly aid in the diagnosis and classification of human disease states.