Computer-aided diagnosis shown highly effective at detecting colon cancer

Researchers at Washington University in St. Louis are developing a new imaging technique that can provide accurate, real-time, computer-aided diagnosis of colorectal cancer, the university reports.
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, according to the article

Compared with pathology reports, they were able to identify tumors with 100% accuracy in this pilot study, the university reports.

Current colon cancer screening is performed by flexible colonoscopy. The procedure involves visual inspection of the mucosal lining of the colon and rectum with a camera mounted on an endoscope. Abnormal appearing areas are then biopsied for analysis, the article states

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, a biomedical engineer in the McKelvey School of Engineering at Washington University in St. Louis, and Yifeng Zeng, a biomedical engineering doctoral student, are developing the new imaging technique that employs computer-aided diagnosis. Zeng is a BMES member.  

Results appear in an advanced 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 of Engineering 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. 

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