Stefan Mileschin

92 percent accuracy

Salesforce has been showing off its ReceptorNet, a machine learning system which it said can spot a critical biomarker for cancer 92 percent of the time.

The tech company has been working in a partnership with clinicians at the University of Southern California's Lawrence J. Ellison Institute for Transformative Medicine of USC.

According to the popular science magazine Nature Communications, ReceptorNet 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. When the hormones estrogen and progesterone attach to these receptors, they fuel the cancer growth.

These types of biopsy images are less widely available and require a pathologist to review. The immunohistochemistry process favoured by clinicians, which requires a microscope, tends to be expensive and not readily available in parts of the world,

ReceptorNet determines hormone receptor status via hematoxylin and eosin (H&E) staining, which considers the shape, size, and structure of cells. Salesforce researchers trained the system on several thousand H&E image slides from cancer patients in "dozens" of hospitals around the world.

https://fudzilla.com/news/ai/52039-s...an-spot-cancer