Quing Zhu, professor of biomedical engineering in the McKelvey School of Engineering at Washington University in St. Louis, and Yifeng Zeng, a biomedical engineering doctoral student, are developing a new imaging technique that can provide accurate, real-time, computer-aided diagnosis of colorectal cancer.
Using deep learning, a type of machine learning, researchers 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 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.
The investigational technique is based on optical coherence tomography (OCT), an optical imaging technology that has been used for two decades in ophthalmology to take images of the retina. However, engineers in the McKelvey School and elsewhere have been advancing the technology for other uses since it provides high spatial and depth resolution for up to 1- to 2-millimeter imaging depth. OCT detects the differences in the way health and diseased tissue refract light and is highly sensitive to precancerous and early cancer morphological changes. When further developed, the technique could be used as a real-time, noninvasive imaging tool alongside traditional colonoscopy to assist with screening deeply seated precancerous polyps and early-stage colon cancers.
“We think this technology, combined with the colonoscopy endoscope, will be very helpful to surgeons in diagnosing colorectal cancer,” said Zhu, the paper’s senior author who also is a professor of radiology at the Mallinckrodt Institute of Radiology at Washington University School of Medicine. “More research is necessary, but the idea is that when the surgeons use colonoscopy to examine the colon surface, this technology could be zoomed in locally to help make a more accurate diagnosis of deeper precancerous polyps and early-stage cancers versus normal tissue.”
Two years ago, Zeng, the paper’s lead author, began using OCT as a research tool to image samples of colorectal tissue removed from patients at the School of Medicine. He observed that the healthy colorectal tissue had a pattern that looked similar to teeth. However, the precancerous and cancerous tissues rarely showed this pattern. The teeth pattern was caused by light attenuation of the healthy mucosa microstructures of the colorectal tissue.
Zhu, Zeng and the team, in collaboration with Chao Zhou, associate professor of biomedical engineering, are now developing a catheter that could be used simultaneously with the colonoscopy endoscope to analyze the teeth-like pattern on the surface of the colon tissue and to provide a score of probability of cancer from RetinaNet to the surgeons.
