New topology-based biomarkers may improve breast cancer prediction

For decades, pathologists have diagnosed and graded breast cancer by looking at tissue samples under a microscope, searching for telltale signs of disorder in the structure of cells and tissues. Now, researchers at Columbia and their collaborators have developed a new computational approach that transforms those visual patterns into quantitative measurements, potentially improving how clinicians predict breast cancer outcomes and choose therapies.

Excessive weight gain during pregnancy increases risk of serious complications, no matter your weight

A study of more than 1 million women has found that excessive weight gain during pregnancy is linked to an increased risk of potentially life-threatening complications, regardless of whether starting weights were considered underweight, normal, overweight or obese. The study found that the increased risk was between 16% and 33%, and the highest risk was among those considered underweight at the start of their pregnancy.

AI detection not automatically better for colorectal cancer screening in Lynch syndrome, study shows

People with Lynch syndrome, the most common hereditary predisposition to colorectal cancer, face a markedly increased cancer risk and therefore undergo regular colonoscopies. Researchers from the University Hospital Bonn (UKB), the University of Bonn, the University of Leipzig, and Amsterdam UMC investigated whether artificial intelligence improves the detection of precancerous lesions. Their findings show that, in specialized centers, AI provided no significant additional benefit. The study was published in The Lancet Gastroenterology & Hepatology.

When healing injuries, timing of regenerative cues matters

For decades, medicine has chased a simple but elusive goal of delivering the right drug to the right place at the right time. New research from the University of Oregon suggests that when it comes to healing from injury, the timing of regenerative cues might be even more important than previously realized.