Clinical, Non-invasive in Vivo Diagnosis of Skin Cancer Using Multimodal Spectral Diagnosis
| Author | : Liang Lim |
| Publisher | : |
| Total Pages | : 192 |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
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The goal of this thesis is to study the potential of optical spectroscopy as a clinical diagnostic tool for melanoma and nonmelanoma skin cancer. Skin cancer is the most common cancer in the United States. Like most cancers, early diagnosis and treatment improves patient prognosis for both melanoma and nonmelanoma skin cancer. However, current "gold standard" for diagnosis is invasive, costly and time-consuming. A diagnostic procedure consists of a clinical examination of the suspicious lesion, followed by biopsy and histopathology, with an additional turnaround time of approximately one week. There is a need for an accurate, objective, noninvasive, and faster method to aid physician in diagnosing cancerous lesions, increasing diagnosis accuracy while preventing unnecessary biopsies. We propose Spectral Diagnosis, a system capable of noninvasive in vivo spectroscopic examination of human skin. The research objectives are: (1) Probe pressure effects on in vivo spectroscopy measurements of human skin, (2) Clinical trial of Spectral Diagnosis, (3) Design, construction, and characterization of a confocal Raman microspectroscope. Spectral Diagnosis utilizes an optical fiber probe that transmits and collects optical spectra in contact with the suspected lesion. We identified short term and light probe pressure effects to be minimal on diagnostic parameters, and should not negatively influence diagnostic performance. We conducted a clinical trial at the University of Texas MD Anderson Cancer Center, and our results show that principal components from three spectroscopy modalities (diffuse reflectance spectroscopy, laser induced fluorescence spectroscopy, and Raman spectroscopy) provide excellent melanoma and nonmelanoma skin cancer diagnosis. We also constructed and characterized a Raman microspectroscope, with the goal of developing a physiological-based fitting model to better understand the analysis of in vivo Raman spectroscopy data from human skin tissue.
