Advances in Optical Gastrointestinal Endoscopy: A Technical Review

Authors

Yubo Tang
Sharmila Anandasabapathy
Rebecca Richards-Kortum

Publication Date

10-1-2021

Journal

Molecular Oncology

DOI

10.1002/1878-0261.12792

PMID

32915503

PMCID

PMC8486567

PubMedCentral® Posted Date

9-19-2020

PubMedCentral® Full Text Version

Post-Print

Published Open-Access

yes

Keywords

Artificial Intelligence, Endoscopy, Gastrointestinal, Gastrointestinal Neoplasms, Humans, Optical Imaging, Precancerous Conditions, gastrointestinal tract, machine learning, molecular probe, optical endoscopy

Abstract

Optical endoscopy is the primary diagnostic and therapeutic tool for management of gastrointestinal (GI) malignancies. Most GI neoplasms arise from precancerous lesions; thus, technical innovations to improve detection and diagnosis of precancerous lesions and early cancers play a pivotal role in improving outcomes. Over the last few decades, the field of GI endoscopy has witnessed enormous and focused efforts to develop and translate accurate, user-friendly, and minimally invasive optical imaging modalities. From a technical point of view, a wide range of novel optical techniques is now available to probe different aspects of light-tissue interaction at macroscopic and microscopic scales, complementing white light endoscopy. Most of these new modalities have been successfully validated and translated to routine clinical practice. Herein, we provide a technical review of the current status of existing and promising new optical endoscopic imaging technologies for GI cancer screening and surveillance. We summarize the underlying principles of light-tissue interaction, the imaging performance at different scales, and highlight what is known about clinical applicability and effectiveness. Furthermore, we discuss recent discovery and translation of novel molecular probes that have shown promise to augment endoscopists' ability to diagnose GI lesions with high specificity. We also review and discuss the role and potential clinical integration of artificial intelligence-based algorithms to provide decision support in real time. Finally, we provide perspectives on future technology development and its potential to transform endoscopic GI cancer detection and diagnosis.