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Robotic Eyes Spot Cancer


New software developed by researchers at Stanford University shows computers may be better than humans at diagnosing cancer, reports Popular Science's Rebecca Boyle. The system, called C-Path for Computational Pathologist, automatically evaluates microscopic images of breast cancer to determine its type and make a prognosis, Boyle says. And as the researchers found in their new study published in Science Translational Medicine, the algorithm is more accurate than a human doctor could be. C-Path "can classify the types of cancer cells present, and even identified a new set of features that are associated with a poor chance of survival," Boyle says. The researchers developed C-Path using existing tissue samples, and then human pathologists taught it to distinguish stromal and epithelial cells. When the researchers checked the machine's accuracy against a set of validation samples, "its results were a statistically significant improvement over human-based examination," Boyle adds. "C-Path even figured out something pathologists haven't — that the characteristics of the cancer cells and the surrounding cells were both important in determining a patient's outcome."

The Scan

Driving Malaria-Carrying Mosquitoes Down

Researchers from the UK and Italy have tested a gene drive for mosquitoes to limit the spread of malaria, NPR reports.

Office Space to Lab Space

The New York Times writes that some empty office spaces are transforming into lab spaces.

Prion Pause to Investigate

Science reports that a moratorium on prion research has been imposed at French public research institutions.

Genome Research Papers on Gut Microbe Antibiotic Response, Single-Cell RNA-Seq Clues to Metabolism, More

In Genome Research this week: gut microbial response to antibiotic treatment, approach to gauge metabolic features from single-cell RNA sequencing, and more.