Physiological model using diffuse reflectance spectroscopy for nonmelanoma skin cancer diagnosis. Issue 12 (2nd September 2019)

• Record Type: Journal Article
• Authors: Zhang, Yao; Moy, Austin J.; Feng, Xu; Nguyen, Hieu T. M.; Reichenberg, Jason S.; Markey, Mia K.; Tunnell, James W.
• Is Part Of: Journal of biophotonics. Volume 12:Issue 12(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-09-02
• Subjects: classification -- diffuse reflectance spectroscopy -- Monte Carlo look‐up table model -- physiological basis -- skin cancer; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201900154
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: Diffuse reflectance spectroscopy (DRS) is a noninvasive, fast, and low‐cost technology with potential to assist cancer diagnosis. The goal of this study was to test the capability of our physiological model, a computational Monte Carlo lookup table inverse model, for nonmelanoma skin cancer diagnosis. We applied this model on a clinical DRS dataset to extract scattering parameters, blood volume fraction, oxygen saturation and vessel radius. We found that the model was able to capture physiological information relevant to skin cancer. We used the extracted parameters to classify (basal cell carcinoma [BCC], squamous cell carcinoma [SCC]) vs actinic keratosis (AK) and (BCC, SCC, AK) vs normal. The area under the receiver operating characteristic curve achieved by the classifiers trained on the parameters extracted using the physiological model is comparable to that of classifiers trained on features extracted via Principal Component Analysis. Our findings suggest that DRS can reveal physiologic characteristics of skin and this physiologic model offers greater flexibility for diagnosing skin cancer than a pure statistical analysis. Physiological parameters extracted from diffuse reflectance spectra data for nonmelanoma skin cancer diagnosis. Abstract : This study developed a physiological model and tested its capability for noninvasive, fast, and low‐cost nonmelanoma skin cancer diagnosis with a clinical spectral dataset. This model was able to capture physiological information relevant to skin cancer, such as scattering parameters, blood volume fraction, oxygen saturation and vessel radius. This physiologic model not only reveals physiologic characteristics of skin but also offers great flexibility for diagnosing skin cancer.