TOP: STEVE FYFFE / STANFORD NEWS SERVICE; STEVE FYFFE / STANFORD NEWS SERVICE
The Learned Machine Since 1928, breast cancer characteristics have been evaluated and categorized by pathologists looking through a microscope. They examine and score the cancers according to a scale developed eight decades ago. The scores help doctors assess the type and severity of the cancer, and to calculate the patient’s prognosis and course of treatment. Daphne Koller, an associate professor of computer science, and pathologists at the Stanford School of Medicine have for the first time trained computers to analyze microscopic images of breast cancers with greater prognostic accuracy than humans. To do this, Koller’s computers pore over images of tissue samples from patients whose prognosis is known. Time and time again, the computer measures and compares various structures of the tumors and surrounding tissues, and tries to predict patient survival. Those predictions are compared against the known patient data. Then, depending upon how accurate they are, the computers adapt. It is essentially trial and error, but at a much accelerated rate. Gradually, the computers figure out what tumor structures best predict survival. “The computer learns,” says Koller. Pathologists have been trained to look at and evaluate specific cellular structures of known clinical importance, which get incorporated into the grade, explains Andrew Beck, MD, a doctoral candidate in biomedical informatics who worked with Koller on the research. But tumors contain innumerable additional features whose clinical significance has not previously been evaluated. “The computer strips away that bias and looks at
thousands of factors to determine which matter most in predicting survival,” says Koller. Their model is called Computational Pathologist, or C-Path. It assesses not three or four or even a handful of structures, but 6,642 cellular factors. In the end, C-Path yields results that are a statistically significant improvement over human-based evaluation.
In a discovery that may prove even more valuable than improved prognoses, C-Path identified structural features in cancers that matter as much or more than those on which pathologists have traditionally relied. The computers confirm, for instance, � S T A N F O R D
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