The researchers extracted the DNA from the samples and then used high-tech gene microarray equipment to analyze 8,064 genes to determine the level at which they were present in each colon sample, according to Florin M. Selaru, M.D., research associate in the Department of Medicine at the University of Maryland School of Medicine, director of bioinformatics and data analysis at the Greenebaum Cancer Center, and the lead author of the study.
These "gene expression" levels were translated into numbers, which were processed by "artificial neural networks, multi-layer mathematical programs that operate much like the human brain and are capable of recognizing complex patterns in large amounts of data.
Using gene information from 27 of the 39 samples, researchers "trained" the neural network to recognize the two types of colon cancer, and then gave it information from 12 samples it had never seen. It made the correct diagnosis in all 12 cases.
"We now have a tool that is extremely precise, which may prevent misdiagnoses and unnecessary surgeries and help us treat patients most effectively," says Dr. Selaru, who developed the computer algorithm used in the study.
The researchers were also able to reduce the number of genes necessary to make the correct diagnosis from 8,064 to 97, which would make the method easier and less expensive if this technology became more widely available.
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Contact: Karen Warmkessel
kwarmkessel@umm.edu
410-328-8919
University of Maryland Medical Center
25-Feb-2002