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Date Available

1-15-2027

Year of Publication

2026

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Engineering

Department/School/Program

Biomedical Engineering

Faculty

Caigang Zhu

Faculty

Sridhar Sunderam

Abstract

Radiotherapy remains a primary treatment modality for several cancers, including head and neck cancers (HNC). Its efficacy, however, is frequently constrained by the emergence of radiation resistance in certain tumors. Tumor hypoxia and rapid reoxygenation are recognized as principal contributors to this resistance. Additionally, metabolic reprogramming is a critical factor in radiotherapy failure in HNC. Comprehensive and longitudinal characterization of the interplay between vascular and metabolic factors is therefore essential for improved prediction of treatment outcomes and informed therapeutic decision-making. Current methodologies are limited in their ability to enable rapid and repeated monitoring. For instance, assays such as Seahorse and metabolomics are inherently destructive, precluding repeated measurements on the same sample. Imaging modalities, including PET and MRI, typically assess a limited number of endpoints per session and are often costly, require specialized expertise, and are confined to core facilities, restricting their accessibility for frequent assessments. In contrast, optical spectroscopic and imaging techniques provide a non-destructive and cost-effective means to rapidly quantify multiple metabolic and vascular endpoints simultaneously. This dissertation advances the development of a low-cost, portable, multiparametric microscopy platform (Chapter 2) that integrates dark-field and fluorescence microscopy. The system enables imaging of metabolic parameters, such as glucose uptake and mitochondrial function, using exogenous fluorescent probes, including 2-NBDG and TMRE. A spectral image-processing algorithm was also developed to quantify tissue oxygenation and total hemoglobin content from dark-field-based diffuse reflectance spectral images. The platform's in vivo feasibility was demonstrated in an orthotopic tongue tumor model of HNC (SCC-61). Accurate quantification of vascular and metabolic parameters, however, requires precise measurement of tissue optical properties, as absorption and scattering can distort measured signals. Furthermore, the clinical applicability of exogenous probes (e.g., 2-NBDG and TMRE) is limited by regulatory and safety considerations. To address these challenges, Chapter 3 details the development of a portable diffuse reflectance spectroscopy system to quantify tissue optical properties and vascular parameters in orthotopic HNC (SCC-61) tongue tumor models. This system employs a gold-standard Monte Carlo inversion model for parameter estimation and was used to characterize tumors at various developmental stages, including early and advanced. Chapter 4 introduces a combined diffuse reflectance spectroscopy and autofluorescence spectroscopy platform. Autofluorescence spectroscopy was used to estimate the optical redox ratio, which reflects the balance between glycolytic and mitochondrial metabolism, based on the endogenous coenzymes NADH and FAD. Notably, this method does not require external fluorescent probes. Additionally, robust spectral algorithms were developed to rapidly quantify tissue oxygenation, total hemoglobin content, and the distortion-free optical redox ratio. Finally, a matched HNC model using radiation-sensitive (SCC-61) and radiation-resistant (rSCC-61) cell lines was established in orthotopic tongue tumor models for comparative analysis. Collectively, these optical technologies offer significant potential for longitudinal monitoring of therapeutic response and treatment outcomes in HNC patients.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2026.354

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Archival

Funding Information

This study is supported by the National Institute of Dental and Craniofacial Research (NIDCR) and the National Institute of General Medical Sciences (NIGMS) (U.S. National Institutes of Health, R01 DE031998, 2023-2028), and by the University of Kentucky Startup (2019-2024). 

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Available for download on Friday, January 15, 2027

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