Multi-Functional Optical Coherence Tomography Assesses Skin Involvement in Systemic Sclerosis

Systemic sclerosis (SSc), or scleroderma, is a chronic autoimmune disorder characterized by fibrosis in the skin and internal organs. SSc has a five-year post-diagnosis survival rate of 77.9%, declining to 55.5% after ten years, and a median survival of approximately 11 years, making early diagnosis and classification important for assessing disease progression and patient survival. The modified Rodnan skin score (mRSS) is the standard for evaluating skin severity in SSc, involving palpation of 17 body sites with scores from 0 (normal) to 3 (severe). Despite being the standard, mRSS requires extensive training for accurate scoring. Thus, an objective tool for evaluating skin fibrosis could revolutionize clinical trials and patient management by improving treatment approvals. My doctoral research focuses on developing a novel technique utlizing optical coherence tomography (OCT) to assess the skin's morphological, mechanical, and microvascular characteristics in SSc patients. OCT is a noninvasive imaging modality with optimal depth (1-2 mm) and resolution (5-15 μm) for detailed characterization of skin fibrosis. It quantitatively measures various tissue parameters, making it well-suited for detecting SSc-related changes. Optical coherence elastography (OCE), an extension of OCT, can enhance assessment by measuring the mechanical properties of skin. Additionally, OCT angiography (OCTA), another extension of OCT, can map the microvasculature affected by SSc, offering another dataset. In my dissertation, I discuss the use of multifunctional OCT for assessing skin involvement in SSc patients. A multifunctional OCT system was developed to detect structural, mechanical, and vascular changes in the skin. Its reliability and operational performance were tested in a murine model of SSc-like fibrosis. The results of the study confirmed that multifunctional OCT could detect alterations in mechanical properties, morphology, and vascular characteristics in murine skin. The system was then converted to a clinic-friendly mobile format at UTHealth. Data was collected from 55 patients and 20 healthy controls, showing high correlation in distinguishing patients from controls but a low-to-moderate correlation with histopathology and qPCR measurements. The next step of this project will be to acquire longitudinal data to evaluate the system's capabilities in detecting changes in skin as the disease progresses in the same cohort of patients.

Name thesis:degree_name

Doctor of Philosophy

Discipline thesis:degree_discipline

Biomedical Engineering

Grantor

University of Houston

Year dc:date.issued

2025

Author dc:creator

• Chawla, Harshdeep Singh 1992-

Advisor dc:contributor.advisor

• Larin, Kirill V.

Committee members dc:contributor.committeemember

• Schultz, Jerome S.
• Mayerich, David
• Mohan, Chandra
• Assassi, Shervin

• Biomedical engineering

Language dc:language.iso

English

Handle dc:identifier.uri

https://hdl.handle.net/10657/19521

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/19521