Once it was trained, Beck et al. used C-Path to evaluate tissues of cancer patients it had not examined before. Again, the researchers already knew the outcome, so they were able to check C-Path’s success rate. Its results were a statistically significant improvement over human-based examination, the authors say.Faster please.
C-Path even figured out something pathologists haven’t — that the characteristics of the cancer cells and the surrounding cells were both important in determining a patient’s outcome. “Through machine learning, we are coming to think of cancer more holistically, as a complex system rather than as a bunch of bad cells in a tumor,” said Dr. Matt van de Rijn, a professor of pathology and co-author of the study, in a statement. “The computers are pointing us to what is significant, not the other way around.”
This is an impressive result, as Rimm explains it, because existing pathological analysis is a very subjective science. Experts make judgments about tissue metastasis and a patient’s overall chances of survival by simply looking at tissue, and their own diagnoses can vary widely, as a 2008 study showed. But a computer model using thousands of times more criteria could be much more consistent.
Despite this success, C-Path is still a long way from clinical use, the authors say.
Wednesday, November 09, 2011
Computerized Pathology May Lead To Cancer Diagnosis/Treatment Breakthroughs
Scientists have been working to create computer programs that can help identify and catalog cancers, which involve abnormalities in various kinds of cells in the human body. After researchers taught the program how to identify cancer cells among the slides, they found that the program not only correctly identified cancer cells, but that characteristics of the cancer cells and those of surrounding cells were important to identifying prognosis for recovery.