Early detection of neoplastic changes remains a critical challenge in clinical cancer diagnosis and treatment. Many cancers arise from epithelial layers such as those of the gastrointestinal (GI) tract. White-light endoscopy guided excisional biopsy and histopathology is currently the gold standard for GI cancer diagnosis. However, it suffers from high false negative rates due to the sampling errors. Furthermore, a significant portion (~10%- 30%) of patients after endoscopic ablative therapeutic treatment showed the presence of metaplasia or dysplasia buried underneath the neo-epithelium, which is associated with the risk of cancer development. Current standard endoscopic technology is unable to detect those subsurface lesions. Therefore, there is a critical need for developing new diagnostic tools which can assess tissue architectural and molecular information across the mucosal depth for improved detection of subsurface cancer, and evaluate the invasion depth of a lesion. Since cancer development is associated with in both morphological and molecular alterations, an imaging technology that can quantitative image tissue's morphological and molecular biomarkers and assess the depth- extent of a lesion in vivo and in real time, without the need for tissue excision, would be a major advance in GI cancer diagnostics and therapy. The objective of this application is to develop a new generation of multi-modal depth-resolved imaging technology for GI cancer detection. This proposal is built upon our novel multi-modal optical imaging platform combining high-resolution optical coherence tomography (OCT) and depth-resolved high-sensitivity fluorescence laminar optical tomography (FLOT) for simultaneous structural and molecular imaging. Our extensive preliminary data on multi-modal imaging of animal models demonstrates the feasibility for simultaneous quantitative imaging of structural and molecular information for more accurate diagnosis and prognosis. To establish the diagnostic ability of OCT/FLOT for GI cancer detection, we propose to pursue an Exploratory/Developmental Research (R21) to pursue three specific aims: 1) Image an animal model of GI cancer using OCT/FLOT and obtain the correlated histopathological diagnosis. 2) Develop quantitative structural and molecular imaging parameters from co-registered OCT/FLOT images for multi-parametric analysis. 3) Establish the sensitivity, specificity, and diagnostic accuracy of OCT/FLOT for GI cancer detection, and test the hypothesis of enhanced cancer detection using combined information compared to single modality alone. If successful, this project will result in a fundamentally new non-invasive multi-modal imaging technology for improved GI cancer detection, which can be readily translated into endoscopic imaging in the GI clinics. It is expected to have a major impact on detection, diagnosis, and characterization of GI cancers, as well as a wide range of epithelial cancers.

Public Health Relevance

Early detection of neoplastic changes especially those buried underneath the surface remains a critical challenge in clinical cancer diagnosis and treatment of the gastrointestinal (GI) tract. The objective of this application is to develop a new generation of multi-modal depth-resolved imaging technology for depth- resolved structural and molecular imaging for enhanced GI cancer detection. If successful, this project will result in a new non-invasive multi-modal imaging technology that can be readily translated into endoscopic imaging of GI cancers, as well as a wide range of epithelial cancers.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21EB012215-02
Application #
8250371
Study Section
Special Emphasis Panel (ZRG1-SBIB-J (80))
Program Officer
Krosnick, Steven
Project Start
2011-04-01
Project End
2014-03-31
Budget Start
2012-04-01
Budget End
2014-03-31
Support Year
2
Fiscal Year
2012
Total Cost
$211,710
Indirect Cost
$49,352
Name
University of Maryland College Park
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
790934285
City
College Park
State
MD
Country
United States
Zip Code
20742
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