Tissue biopsy slides stained using hematoxylin and eosin (H&E) dyes are a cornerstone of histopathology, especially for pathologists needing to diagnose and determine the stage of cancers. A research team led by MIT scientists at the Media Lab, in collaboration with clinicians at Stanford University School of Medicine and Harvard Medical School, now shows that digital scans of these biopsy slides can be stained computationally, using deep learning algorithms trained on data from physically dyed slides.

Pathologists who examined the computationally stained H&E slide images in a blind study could not tell them apart from traditionally stained slides while using them to accurately identify and grade prostate cancers. What’s more, the slides could also be computationally “de-stained” in a way that resets them to an original state for use in future studies, the researchers conclude in their May 20 study published in JAMA Network Open.

This process of computational digital staining and de-staining preserves small amounts of tissue biopsied from cancer patients and allows researchers and clinicians to analyze slides for multiple kinds of diagnostic and prognostic tests, without needing to extract additional tissue sections.

“Our development of a de-staining tool may allow us to vastly expand our capacity to perform research on millions of archived slides with known clinical outcome data,” says Alarice Lowe, an associate professor of pathology and director of the Circulating Tumor Cell Lab at Stanford University, who was a co-author on the paper. “The possibilities of applying this work and rigorously validating the findings are really limitless.”

The researchers also analyzed the steps by which the deep learning neural networks stained the slides, which is key for clinical translation of these deep learning systems, says Pratik Shah, MIT

Spurred by the COVID-19 pandemic, Princeton researchers have developed a diagnostic tool to analyze chest X-rays for patterns in diseased lungs. The new tool could give doctors valuable information about a patient’s condition, quickly and cheaply, at the point of care.

Jason Fleischer, professor of electrical engineering and the project’s principal investigator, said he was inspired to create the tool after reading about COVID-19’s devastating range of attacks. As hospitals have been overrun with patients, doctors have observed two basic types of lung damage, one more immediately life-threatening than the other. Treatment can differ between the types, so distinguishing the two could improve care and better allocate scarce resources.

While current differentiation methods involve expensive and time-consuming procedures, such as computed tomography (CT) scans, Fleischer’s machine learning model looks at a simple X-ray image and finds patterns that are too subtle even for the expert human eye. This tool would give doctors a new measure for determining the type and severity of COVID-19 pneumonia. And the process, on the ground, is simple.

“Importantly, there is no change in practice,” Fleischer said. “The technician doesn’t have to do anything differently. Hospitals don’t have to do any new procedure. With the X-rays they already have—and routinely take—we can give them this extra information.”

Fleischer and graduate student Mohammad Tariqul Islam posted a paper detailing their work on medrxiv (pronounced med archive), a server for scientists to share results in the form of early drafts while a paper undergoes the formal editorial process. At the time of this writing, Fleischer’s paper “Distinguishing L and H phenotypes of COVID-19 using a