A Stanford University School of Medicine study shows that computers can be trained to assess slides of lung cancer tissues and predict patient survival times.
The researchers found that a machine-learning approach to identifying critical disease-related features accurately differentiated between two types of lung cancers and predicted patient survival times better than the standard approach of pathologists classifying tumors by grade and stage.
“Pathology as it is practiced now is very subjective,” said Michael Snyder, PhD, professor and chair of genetics. “Two highly skilled pathologists assessing the same slide will agree only about 60 percent of the time. This approach replaces this subjectivity with sophisticated, quantitative measurements that we feel are likely to improve patient outcomes.”
The research was published Aug. 16 in Nature Communications. Snyder, who directs the Stanford Center for Genomics and Personalized Medicine, shares senior authorship of the study with Daniel Rubin, MD, associate professor of radiology and of medicine. Graduate student Kun-Hsing Yu, MD, is the lead author of the study.
Although the current study focused on lung cancer, the researchers believe that a similar approach could be used for many other types of cancer.