Although this is the current standard of care, it does have its shortcomings. First, this technique relies on visual detection, but small lesions are hard to detect with the naked eye, and early malignancies are often missed. Second, visual endoscopy can only detect changes in the surface of the bowel wall, not in its deeper layers.
Quing Zhu, Prof. 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.
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 Prof. 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."
Zhu and her team collaborated with Matthew Mutch, MD, chief of colon and rectal surgery; William C. Chapman Jr., MD, a resident in colon and rectal surgery; and Deyali Chatterjee, MD, assistant professor of pathology & immunology, all at the School of Medicine.
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.
Zeng began working with another graduate student, Shiqi Xu, who earned a master's in electrical engineering from McKelvey Engineering in 2019 and is co-first author on the paper, to train RetinaNet, a neural network model of the brain where neurons are connected in complex patterns to process data, to recognize and learn the patterns in the tissue samples.
They trained and tested the network using about 26,000 OCT images acquired from 20 tumor areas, 16 benign areas and six other abnormal areas in patient tissue samples. Pathology residents Zahra Alipour and Heba Abdelal assisted with the comparison. The team found a sensitivity of 100% and a specificity of 99.7%.
MEDICA-tradefair.com; Source: McKelvey School of Engineering at Washington University in St. Louis