WEDNESDAY, Nov. 9 (HealthDay News) -- A new computer model
analyzes microscopic breast cancer images and predicts patient
survival better than the pathologists who do the job now, new
The computer program "provides information above and beyond what
the physician provides, using the same data," said Daphne Koller, a
professor of computer science at Stanford University and senior
author of the study. The computer model is called Computational
Pathologist, or C-Path.
Although the technique needs much more study, it may someday
supplement analysis of breast cancer by pathologists, who use a
process that has remained largely unchanged for 80 years, said
With the traditional method, pathologists examine a tumor
visually under a microscope and score it according to an
established scale. The scores help doctors figure out the type and
diversity of the cancer. From that information, they calculate the
outlook and course of treatment.
The pathologists' determination relies on three factors: what
percent of the tumor is made up of tube-like cells; the diversity
of the nuclei in the outer cells; and how often those cells
But the system is subjective, said the study authors, whose
computer model found that additional factors may influence
survival. The study is published Nov. 9 in
Science Translational Medicine.
Using the C-Path image analysis program, Koller's team trained
the computer to analyze biopsied breast cancer tissue and to
determine the cancer features that matter most and least in
The computer model looked at more than 6,000 cellular factors
and found that characteristics of the cells surrounding the cancer
-- the cancer's environment -- are also important in predicting
"We found 11 [factors] ultimately that showed the most robust association with survival," said Dr. Andrew H. Beck, an assistant professor of pathology at Harvard Medical School, in Boston. He is the lead author of the study, done while he was a doctoral student at Stanford School of Medicine.
The researchers applied the C-Path system to images from two
groups of patients with breast cancer. One group, from the
Netherlands, included 248 people. The other, from Vancouver,
Canada, had 328 patients.
The prognostic score generated by C-Path was strongly associated
with survival in both groups, Koller said. The groups predicted to
be high risk by C-Path were more likely to die in a given year than
those predicted to be low risk, she said.
C-Path would help, not replace, pathologists. "We see it
ultimately as a complementary approach," said Beck.
Neither Stanford nor the researchers has begun commercial
development of the method, Koller said.
In a commentary accompanying the study, Dr. David L. Rimm,
professor of pathology at Yale University School of Medicine,
called the research ''landmark" work.
"C-Path potentially is the first truly objective, quantitative grading system for cancerous tissue and its surrounding stroma [connective tissue]," he writes.
However, Rimm and other experts agreed that further research is
needed. "This isn't quite ready for prime time," he said.
Dr. Anil V. Parwani, a pathologist and division director of
pathology informatics at the University of Pittsburgh Medical
Center, said: "It (C-Path) has interesting potential and provides a
novel look. The results are interesting, but the study is
To learn more about breast cancer, visit the
College of American Pathologists.
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