A research team from the lab of Quing Zhu, the Edwin H. Murty Professor of Engineering within the Department of Biomedical Engineering on the McKelvey School of Engineering at Washington University in St. Louis, has combined optical coherence tomography (OCT) and machine learning to develop a colorectal cancer imaging tool which will sooner or later improve the normal endoscopy currently utilized by doctors.

The outcomes were published within the June issue of the Journal of Biophotonics, with a picture featured on the within cover.

Screening for colon cancer now relies on human visual inspection of tissue during a colonoscopy procedure. This system, nevertheless, doesn’t detect and diagnose subsurface lesions.

An endoscopy OCT essentially shines a light-weight within the colon to assist a clinician see deeper to visualise and diagnose abnormalities. By collaborating with physicians at Washington University School of Medicine and with Chao Zhou, associate professor of biomedical engineering, the team developed a small OCT catheter, which uses an extended wavelength of sunshine, to penetrate 1-2 mm into the tissue samples.

Hongbo Luo, a PhD student in Zhu’s lab, led the work.

The technique provided more details about an abnormality than surface-level, white-light images currently utilized by physicians. Shuying Li, a biomedical engineering PhD student, used the imaging data to coach a machine learning algorithm to distinguish between “normal” and “cancerous” tissue. The combined system allowed them to detect and classify cancerous tissue samples with a 93% diagnostic accuracy.

Zhu is also a professor of radiology on the School of Medicine. Her team worked with Vladimir Kushnir and Vladimir Lamm on the School of Medicine, Zhu’s team of PhD students, including Tiger Nie, began a trial in patients in July 2022.

Source:

Washington University in St. Louis

Journal reference:

Luo, H., et al. (2022) Human colorectal cancer tissue assessment using optical coherence tomography catheter and deep learning. Journal of Biophotonics. doi.org/10.1002/jbio.202100349.