When it comes to the future of diagnosing and treating cancer, computers – not humans – could hold the key to delivering the best quality precision medicine. A new study out of the Stanford University School of Medicine has found that computers can be trained to more accurately assess slides of lung cancer tissue than pathologists. "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 the subjectivity with sophisticated, quantitative measurements that we feel are likely to improve patient outcomes."

Snyder expects that machine learning will be able to complement the fields of precision medicine cancer genomics, transcriptomics and proteomics. "We launched this study because we wanted to begin marrying imaging to our'omics' studies to better understand cancer processes at a molecular level," he said. "This brings cancer pathology into the 21st century and has the potential to be an awesome thing for patients and their clinicians.