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C-Path Computational Pathologist Better Than Doctors
C-Path is a computational pathologist developed by computer science researchers and (human) pathologists at Stanford University.
Researchers trained C-Path using existing tissue samples whose prognosis was already known. The computer examined images and measured tumors and other structures in an effort to predict patient survival likelihood. After comparing its results against known data, the machine-learning-based system improved over time.
“Pathologists have been trained to look at and evaluate specific cellular structures of known clinical importance, which get incorporated into the grade. However, tumors contain innumerable additional features, whose clinical significance has not previously been evaluated,” said Andrew Beck, MD, a doctoral candidate in biomedical informatics and the paper’s first author.
“The computer strips away that bias and looks at thousands of factors to determine which matter most in predicting survival,” said Koller.
C-Path, in fact, assesses 6,642 cellular factors. Once trained using one group of patients, C-Path was asked to evaluate tissues of cancer patients it had not checked before and the result was compared against known data. Ultimately, C-Path yielded results that were a statistically significant improvement over human-based evaluation.
Science fiction fans may be thinking of devices like the autodoc from Larry Niven's (still!) excellent 1970 novel Ringworld and the
crechepod from Frank Herbert's neglected 1972 novel The Godmakers. Both of these devices were capable of autonomous medical care.
Via Stanford.
Medical science has long used three specific features for evaluating breast cancer cells — what percentage of the tumor is comprised of tube-like cells, the diversity of the nuclei in the outermost (epithelial) cells of the tumor and the frequency with which those cells divide (a process known as mitosis). These three factors are judged by sight with a microscope and scored qualitatively to stratify breast cancer patients into three groups that predict survival rates.
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