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Human-Machine Collaborators Explore the Ecology of Cancer

Posted By Prucia Buscell, Thursday, November 17, 2011

Stanford University researchers have "taught" computers to analyze microscopic breast cancer cells, and an early study shows they are slightly more accurate than human pathologists in predicting patient survival. The computer analysis lends support to the idea that cancer is an ecosystem and that the entire microenvironment of a tumor needs evaluation.

A Stanford website story by Andrew Myers explains that computer scientists at the Stanford School of Engineering and pathologists at the Stanford School of Medicine collaborated to train to analyze images of malignant tumors. Their model is called C-Path, for Computational Pathologist, and the findings are published in the Nov. 9 issue of Science Translational Medicine. Daphne Koller, professor of computer science and an author of the paper, said the computer "learned" by poring over images of tissue samples taken from patients with known prognoses. Tissue sample images from two groups of breast cancer patients, 248 patients from the Netherlands and 328 patients from Canada were analyzed.

Myers's story points out pathological analysis of breast cancer hasn't changed much since it was developed in 1928. Pathologists have been trained to look at what percent of the tumor is made up of tube-like cells, the diversity of the outermost cells of the tumor, and the frequency with which the cells divide. These factors are then scored to assign patients into three groups that predict survival rates. C-Path looked at 6,642 cellular factors, and found that characteristics in the cells surrounding the tumor were important in predicting survival.

"We built a model based on features of the stroma - the microenvironment between cancer cells - that was a stronger predictor of outcome than the one built exclusively from features of the epithelial (outermost) cells," said Andrew Beck, an assistant professor of pathology at Harvard Medical School who participated in the Stanford research when he was a doctoral student.

"Through machine learning, we are coming to think of cancer more holistically, as a complex system rather than a bunch of bad cells in a tumor," said Matt van de Rijn, pathology professor and co-author of the study. "We're looking at a future where computers and humans collaborate to improve results for patients across the world."

Koller, described in Newsweek as "on the bleeding edge of the leading edge" in artificial intelligence, thinks that eventually, through human-computer collaboration, computers will learn to identify the best course of treatment for an individual patient; they might also predict whether nonmalignant cell samples might turn cancerous, so that many patients could avoid cancer.

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