Colon Cancer Diagnosis Improved with Machine Learning Approach

Researchers at Washington University in St. Louis are developing a new imaging technique that can reportedly provide accurate, real-time, computer-aided diagnosis of colorectal cancer. Using deep learning, a type of machine learning, the team used the technique on more than 26,000 individual frames of imaging data from colorectal tissue samples to determine the method's accuracy. Compared with pathology reports, they were able to identify tumors with 100% accuracy in this pilot study. This is the first report ("Real-time colorectal cancer diagnosis using PR-OCT with deep learning") using this type of imaging combined with machine learning to distinguish healthy colorectal tissue from precancerous polyps and cancerous tissue. Results appear in advance online publication in the journal Theranostics.