Machines Learn to Detect Breast Cancer
Software that can recognize patterns in data is commonly used by
scientists and economics. Now, researchers in the US have applied similar
algorithms to help them more accurately diagnose breast cancer. The researchers
outline details in the International Journal of
Medical Engineering and Informatics.
Duo Zhou, a biostatistician at pharmaceutical company Pfizer in
New York and colleagues Dinesh Mittal and Shankar Srinivasan of the University
of Medicine and Dentistry of New Jersey, point out that data pattern
recognition is widely used in machine-learning applications in science.
Computer algorithms trained on historical data can be used to analyze current
information and detect patterns and then predict possible future patterns.
However, this powerful knowledge discovery technology is little used in
medicine.
The team suggested that just such an automated statistical
analysis methodology might readily be adapted to a clinical setting. They have
done just that in using an algorithmic approach to analyzing data from breast
cancer screening to more precisely recognize the presence of malignant tumors
in breast tissue as opposed to benign growths or calcium deposits. This could
help improve outcomes for patients with malignancy but also reduce the number
of false positives that otherwise lead patients to unnecessary therapeutic,
chemotherapy or radiotherapy, and surgical interventions.
The machine learning approach takes into account nine
characteristics of a minimally invasive fine needle biopsy, including clump
thickness, uniformity of cell size, adhesions, epithelial cell size, bare cell
nuclei and other factors. Trained on definitive data annotated as malignant or
benign, the system was able to correlate the many disparate visual factors
present in the data with the outcome. The statistical model thus developed
could then be used to test new tissue samples for malignancy.
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