Good news! Cancer is history!
"Salesforce today peeled back the curtains on ReceptorNet, a machine learning system researchers at the company developed in partnership with clinicians at the University of Southern California’s Lawrence J. Ellison Institute for Transformative Medicine of USC. The system, which can determine a critical biomarker for oncologists when deciding on the appropriate treatment for breast cancer patients, achieved 92% accuracy in a study published in the journal Nature Communications. ...
In an effort to address this, Salesforce researchers developed an algorithm ... that can predict hormone-receptor status from inexpensive and ubiquitous images of tissue. Typically, breast cancer cells extracted during a biopsy or surgery are tested to see if they contain proteins that act as estrogen or progesterone receptors. ... But these types of biopsy images are less widely available and require a pathologist to review.
In contrast to the immunohistochemistry process favored by clinicians, which requires a microscope and tends to be expensive and not readily available in parts of the world, [instead] ReceptorNet determines hormone receptor status via [inexpensive] hematoxylin and eosin (H&E) staining, which takes into account the shape, size, and structure of cells. ..."
"... What makes ReceptorNet unique is that it focuses on improving the way treatment decisions are made for breast cancer patients. Specifically, ReceptorNet predicts hormone-receptor status from an inexpensive and ubiquitous tissue image. That’s in contrast to the current standard of care, which requires both a more expensive, less widely available type of tissue image — and a trained pathologist to review those images. ..."
Here is the respective research paper:
No comments:
Post a Comment