Abstract

Colorectal cancer diagnosis currently relies on histological detection of endoluminal neoplasia in biopsy specimens. However, clinical visual endoscopy provides no quantitative subsurface cancer information. In this ex vivo study of nine fresh human colon specimens, we report the first use of quantified subsurface scattering coefficient maps acquired by swept-source optical coherence tomography to reveal subsurface abnormities. We generate subsurface scattering coefficient maps with a novel wavelet-based-curve-fitting method that provides significantly improved accuracy. The angular spectra of scattering coefficient maps of normal tissues exhibit a spatial feature distinct from those of abnormal tissues. An angular spectrum index to quantify the differences between the normal and abnormal tissues is derived, and its strength in revealing subsurface cancer in ex vivo samples is statistically analyzed. The study demonstrates that the angular spectrum of the scattering coefficient map can effectively reveal subsurface colorectal cancer and potentially provide a fast and more accurate diagnosis.

Details

Title
The Angular Spectrum of the Scattering Coefficient Map Reveals Subsurface Colorectal Cancer
Author
Zeng Yifeng 1 ; Rao, Bin 1   VIAFID ORCID Logo  ; Chapman, William C, Jr 2   VIAFID ORCID Logo  ; Nandy Sreyankar 1 ; Rais Rehan 3 ; González Iván 3   VIAFID ORCID Logo  ; Chatterjee Deyali 3 ; Mutch, Matthew 2 ; Zhu Quing 4 

 Washington University, Department of Biomedical Engineering, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002) 
 Section of Colon and Rectal Surgery, Washington University School of Medicine, Department of Surgery, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002) 
 Washington University School of Medicine, Department of Pathology and Immunology, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002) 
 Washington University, Department of Biomedical Engineering, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002); Washington University School of Medicine, Department of Radiology, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002) 
Publication year
2019
Publication date
Dec 2019
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2187014322
Copyright
This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.