Neural net programs diagnose colon tumors

March 5, 2002 | Source: KurzweilAI

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, using artificial neural networks to analyze thousands of genes at one time.
The program 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 in a study to be featured on the cover of the March issue of Gastroenterology, the journal of the American Gastroenterological Association.

Patients with Crohn’s 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 center’s Genomics Core Facility.