Screening for colon cancer now relies on human visual inspection of tissue during a colonoscopy procedure. This technique, however, does not detect and diagnose subsurface lesions. Now, a research team has combined optical coherence tomography (OCT) and machine learning to develop a colorectal cancer imaging tool that may one day improve the traditional endoscopy currently used by doctors.

An endoscopy OCT essentially shines a light in the colon to help a clinician see deeper to visualize and diagnose abnormalities. Biomedical engineers at Washington University in St. Louis (St. Louis, MO, USA) have developed a small OCT catheter, which uses a longer wavelength of light, to penetrate 1-2 mm into the tissue samples. The technique provided more information about an abnormality than surface-level, white-light images currently used by physicians. The researchers used the imaging data to train a machine learning algorithm to differentiate between “normal” and “cancerous” tissue. The combined system allowed them to detect and classify cancerous tissue samples with a 93% diagnostic accuracy. 

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