Peeling away pancreatic cancers' defenses

A combination immunotherapy approach that gets through pancreatic cancers’ extra Read more

Immune cell activation in severe COVID-19 resembles lupus

In severe cases of COVID-19, Emory researchers have been observing an exuberant activation of B cells, resembling acute flares in systemic lupus erythematosus (SLE), an autoimmune disease. The findings point towards tests that could separate some COVID-19 patients who need immune-calming therapies from others who may not. It also may begin to explain why some people infected with SARS-CoV-2 produce abundant antibodies against the virus, yet experience poor outcomes. The results were published online on Oct. Read more

Muscle cell boundaries: some assembly required

The worm C elegans gives insight into muscle cell assembly + architecture Read more

Digital Slide Archive

CAPTCHA some cancer cells

Humans are good at deciphering complex images, compared to computers. Until recently, internet users often needed to verify that they were human by completing a CAPTCHA security check. A familiar variety asked the user to check all the boxes that contain a car, or a street sign.

If we asked random people off the street to look at pathology slides and “quick, check all the boxes that contain tumor cells,” what would happen? The accuracy, compared to a trained pathologist, wouldn’t be very good.

Not as easy as labeling which boxes contain street signs!

This challenge of expertise – crowdsourcing and pathology are not immediately compatible – is what Lee Cooper and colleagues sought to overcome in a recent paper published in Bioinformatics. So they put together something they called “structured crowdsourcing.”

“We are interested in describing how the immune system behaves in breast cancers, and so we built an artificial intelligence system to look at pathology slides and identify the tissue components,” Cooper says.

His group was particularly interested in the aggressive form of breast cancer: triple negative. They used pathology slide images from the Cancer Genome Atlas, a National Cancer Institute resource. The goal was to mark up the slides and label which sections contained tumor, stroma, white blood cells, dead cells etc.

They used social media to recruit 25 volunteers — medical students and pathologists from around the world (Egypt, Bangladesh, Saudi Arabia, United Arab Emirates, Syria, USA). Participants underwent training and used Slack to communicate and learn about how to classify images. They collaborated using the Digital Slide Archive, a tool developed at Emory. Read more

Posted on by Quinn Eastman in Cancer Leave a comment