A new machine learning model can classify lung cancer slides at the pathologist level

Machine learning has improved dramatically in recent years and shown great promise in the field of medical image analysis. A team of research specialists at Dartmouth’s Norris Cotton Cancer Center have utilized machine learning capabilities to assist with the challenging task of grading tumor patterns and subtypes of lung adenocarcinoma, the most common form of the leading cause of cancer-related […]

Continue reading »

Newly identified genetic markers classify previously undetermined glioblastoma tumors

Most glioblastoma tumors are marked by one or two broad mutation patterns, but about 20 percent of the lethal brain tumors have biomarkers that cannot be identified.  Now scientists at Duke Cancer Institute have identified mutations for the vast majority of the remaining 20 percent of uncharacterized tumors, which tend to be especially lethal. The findings indicate that these mutations […]

Continue reading »

Polygenic scores to classify cancer risk

Polygenic risk scores could be useful to stratify the risk of several cancers among patients in medical centers, allowing for the potential discovery of new associations between genes, disease and secondary effects, according to a University of Michigan study. Researchers at U-M’s School of Public Health conducted a phenome-wide association study in 28,260 unrelated, genotyped patients of recent European ancestry […]

Continue reading »