Room: Karl Dean Ballroom C
Purpose: Pancreatic cancer is one of the most lethal cancers. The 5-year survival rate is ~7%. Patient survival time is generally less than 12 months after diagnosis. The 2018 Cancer Statistic estimates 55,400 new cases and 44,330 deaths in 2018. About 95% of pancreatic cancers arise from pancreatic ducts. The key to prolonging and saving a pancreatic cancer patientâ€™s life is early diagnosis and treatment. To detect early stage ductal pancreatic cancers, we built an endoscopic 3-D ultra-high resolution Optical Coherence Tomography (OCT) imaging system. In this abstract we report the physics and details of this homemade OCT imaging system.
Methods: OCT is an imaging technique that uses the coherence effects between the reference light and its reflecting/scattering light passing through biological tissues to reconstruct micrometer level resolution in vivo two- and three-dimensional real time images of the tissue structure. This ultra-high resolution image makes it possible to detect cancers at their very early stages when the cancer cells start forming tiny lesions. Taking advantage of the fact that most pancreatic cancers arise from pancreatic ducts, we designed and built an endoscopic-type OCT imaging system that is analogous to the conventional ultrasound endoscopy except the ultra-high resolution and the imaging source is near infrared optical light instead of ultrasound, with the funding support of 2010 Pelotonia Idea Grant from the Ohio State University.
Results: We have successfully built the endoscopic OCT imaging system. This homemade system is able to image the inside of pancreatic ducts to a depth of ~3 mm into the duct wall. The experiment on a resected patientâ€™s pancreas indicated the ability and feasibility of this imaging system to detect early stage pancreatic cancers.
Conclusion: Our OCT system has the ability and is ready to perform a pilot clinical trial on detecting early stage ductal pancreatic cancers.
Funding Support, Disclosures, and Conflict of Interest: This project was supported by the funding of 2010 Pelotonia Idea Grant from the Ohio State University.