Pilot clinical study for quantitative spectral diagnosis of non-melanoma skin cancer

Abstract

Background: Several research groups have demonstrated the non-invasive diagnostic potential of diffuse optical spectroscopy (DOS) and laser-induced fluorescence (LIF) techniques for early cancer detection. By combining both modalities, one can simultaneously measure quantitative parameters related to the morphology, function and biochemical composition of tissue and use them to diagnose malignancy. The objective of this study was to use a quantitative reflectance/fluorescence spectroscopic technique to determine the optical properties of normal skin and non-melanoma skin cancers and the ability to accurately classify them. An additional goal was to determine the ability of the technique to differentiate non-melanoma skin cancers from normal skin.

Study design: The study comprised 48 lesions measured from 40 patients scheduled for a biopsy of suspected non-melanoma skin cancers. White light reflectance and laser-induced fluorescence spectra (wavelength range = 350-700 nm) were collected from each suspected lesion and adjacent clinically normal skin using a custom-built, optical fiber-based clinical instrument. After measurement, the skin sites were biopsied and categorized according to histopathology. Using a quantitative model, we extracted various optical parameters from the measured spectra that could be correlated to the physiological state of tissue.

Results: Scattering from cancerous lesions was significantly lower than normal skin for every lesion group, whereas absorption parameters were significantly higher. Using numerical cut-offs for our optical parameters, our clinical instrument could classify basal cell carcinomas with a sensitivity and specificity of 94% and 89%, respectively. Similarly, the instrument classified actinic keratoses and squamous cell carcinomas with a sensitivity of 100% and specificity of 50%.

Conclusion: The measured optical properties and fluorophore contributions of normal skin and non-melanoma skin cancers are significantly different from each other and correlate well with tissue pathology. A diagnostic algorithm that combines these extracted properties holds promise for the potential non-invasive diagnosis of skin cancer.

Figure 1

Flowchart describing data acquisition and processing using the clinical system.

Figure 2

Representative in vivo diffuse reflectance spectra (circles) from (a) malignant BCC, (c) pre-cancerous AK and (e) malignant SCC. The thin lines indicate LUT model fit. Also shown are the normal skin site measurements (diamonds). The corresponding intrinsic fluorescence spectra from 337 nm excitation for each cancerous group are shown in panels (b), (d) and (f).

Figure 3

Mean physiological parameters of NMSC and respective normal skin sites measured using the LUT-based model. The error bars represent standard error. Stars indicate statistically significant differences between normal and cancerous skin.

Figure 4

Mean fluorophore contributions of NMSC and respective normal skin sites measured using the intrinsic fluorescence model and linear combination analysis. The error bars represent standard error.

Figure 5

Discrimination of NMSC using logistic regression and leave-1-out validation. (a) Normal skin from BCC lesions. (b) Normal skin from grouped AKs and SCCs. (c) AKs from SCCs.

Figure 5

Discrimination of NMSC using logistic regression and leave-1-out validation. (a) Normal skin from BCC lesions. (b) Normal skin from grouped AKs and SCCs. (c) AKs from SCCs.

Figure 5

Discrimination of NMSC using logistic regression and leave-1-out validation. (a) Normal skin from BCC lesions. (b) Normal skin from grouped AKs and SCCs. (c) AKs from SCCs.

Figure 6

Micrographs showing hematoxylin and eosin-stained sections (100x) from (a) AK (b) BCC and (c) SCC. The bottom row shows images of trichrome-stained sections of (d) normal skin (20x), (e) BCC (10x) and (f) AK (10x); collagen shows up as a blue color. Note abundant collagen in the normal skin image.