A new imaging technique that uses deep learning technology can identify tumors in colorectal tissue samples with 100% accuracy, according to findings published in Theranostics.
The study’s authors tested their technique, pattern recognition optical coherence tomography (PR-OCT), on 26,000 optical coherence tomography (OCT) images, achieving a sensitivity of 100% and a specificity of 99.7%. OCT is typically used to capture images of a patient’s retina, but researchers have started evaluating its efficiency in other areas as well.
Senior author Quing Zhu, PhD, a professor of biomedical engineering at Washington University in St. Louis, and colleagues hope their technique can serve as an “optical biopsy tool” for physicians in the near future.
“We think this technology, combined with the colonoscopy endoscope, will be very helpful to surgeons in diagnosing colorectal cancer,” Zhu said in a prepared statement. “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.”
“The unique part of our system is that we could detect a structural pattern within the image,” lead author Yifeng Zeng, a biomedical engineering doctoral student at Washington University, said in the same statement. “Using OCT, we are imaging something that we can find a pattern across all normal tissues. Then we can use this pattern to classify abnormal and cancerous tissue for accurate diagnosis.”
The full Theranostics study can be downloaded at this link.