In a new study, researchers found that a new imaging technique in development provides an accurate, real-time, computer-aided diagnosis of colon cancer.
Using deep learning, a type of machine learning, the 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, the method identified tumors with 100% accuracy.
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.
The research was conducted by a team at Washington University in St. Louis.
Colon cancer is the second most common type of cancer worldwide, with about 90% of cases occurring in people 50 or older.
Arising from the inner surface, or muscosal layer, of the colon, cancerous cells can penetrate through the deeper layers of the colon and spread to other organs. Left untreated, the disease is fatal.
Currently, doctors use a flexible colonoscopy to perform colon cancer screening.
The procedure involves visual inspection of the mucosal lining of the colon and rectum with a camera mounted on an endoscope. Doctors then biopsy abnormal appearing areas for analysis.
Although this is the current standard of care, it does have its shortcomings.
First, the technique relies on visual detection, but small lesions are hard to detect with the naked eye, and often miss early malignancies.
Second, visual endoscopy can only detect changes in the surface of the bowel wall, not in its deeper layers.
In the study, the team based the new imaging technique on optical coherence tomography (OCT), an optical imaging technology used for two decades in ophthalmology to take images of the retina.
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, doctors could use the technique as a real-time, noninvasive imaging tool alongside traditional colonoscopy to assist with screening deeply seated precancerous polyps and early-stage colon cancers.
The team believes this technology, combined with the colonoscopy endoscope, will be very helpful to surgeons in diagnosing colon cancer.
One author of the study is Quing Zhu, a professor of biomedical engineering in the McKelvey School of Engineering.
The study is published in Theranostics.
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