Detection of precancerous lesions in the oral cavity using oblique polarized reflectance spectroscopy: a clinical feasibility study

Abstract

We developed a multifiber optical probe for oblique polarized reflectance spectroscopy (OPRS) in vivo and evaluated its performance in detection of dysplasia in the oral cavity. The probe design allows the implementation of a number of methods to enable depth resolved spectroscopic measurements including polarization gating, source–detector separation, and differential spectroscopy; this combination was evaluated in carrying out binary classification tasks between four major diagnostic categories: normal, benign, mild dysplasia (MD), and severe dysplasia (SD). Multifiber OPRS showed excellent performance in the discrimination of normal from benign, MD, SD, and MD plus SD yielding sensitivity/specificity values of 100%/93%, 96%/95%, 100%/98%, and 100%/100%, respectively. The classification of benign versus dysplastic lesions was more challenging with sensitivity and specificity values of 80%/93%, 71%/93%, and 74%/80% in discriminating benign from SD, MD, and SD plus MD categories, respectively; this challenge is most likely associated with a strong and highly variable scattering from a keratin layer that was found in these sites. Classification based on multiple fibers was significantly better than that based on any single detection pair for tasks dealing with benign versus dysplastic sites. This result indicates that the multifiber probe can perform better in the detection of dysplasia in keratinized tissues.

Fig. 1

(a) Schematic of the overall system for OPRS. (b) Picture of the clinical system on a wheeled cart. (c) Illustration of the distal end of the OPRS probe: I denotes the illumination fiber; BF labels individual detection fibers where BF1, BF2, and BF3 collected copolarized (parallel) component of tissue scattering and BF1per, BF2per, and BF3per collected cross-polarized (perpendicular) scattering. (d) Artistic three-dimensional rendering and (e) an actual image of the distal end of OPRS after fiber polishing (scale bar: 1 mm). (f) Orientation of polarization transmission axes of polarizing film relative to the fiber array of the OPRS probe.

Fig. 2

OPRS spectra pre- and poststandardization.

Fig. 3

(a) A schematic of experimental setup to quantify depth penetration of OPRS collection fibers. (b) Integrated intensities of scattered signal collected by parallel (solid circle) and perpendicular (dotted square) detection fibers as a function of thickness of Intralipid phantom simulating stromal scattering (${\mu }_s^{\prime }=2{\mathrm{mm}}^{-1}$). (c) Depths at 90% signal saturation for individual collection fibers and polarization gated signals.

Fig. 4

Representative images of biopsied tissue sites confirmed as normal, benign, MD, and SD. (scale bar: $200\mu \mathrm{m}$). Tissue slices were stained with H&E for standard histopathological analysis.

Fig. 5

Mean epithelial and keratin thicknesses of biopsied sites (benign, MD, and SD).

Fig. 6

Averaged spectra for each diagnostic category for parallel ($\parallel$), perpendicular ($\perp$), diffuse ($\parallel +\perp$), and polarization gated ($\parallel -\perp$) reflectance spectra.

Fig. 7

Polarized reflectance spectra normalized to the AUC; all collected spectra for each diagnostic category were first normalized by the AUC and then averaged.

Fig. 8

Diagrams illustrating the most discriminatory wavelengths for all features of interest for each feature binary classification task for unnormalized (a) and AUC normalized (b) spectra. Spectral features of interest are indicated by tickmarks along $x$-axis—a total of 27 marks for each classification task; the $y$-axis shows wavelengths.

Fig. 9

ROC curves for each classification task.

Fig. 10

Permutation test to check for overtraining. Collected spectra were randomly assigned diagnoses while keeping the overall distribution of diagnostic categories the same; samples were randomly shuffled 100 times. The mean and standard deviation of the AUC obtained from the shuffling are presented and compared to the real AUCs shown as stars (all fibers combined), dots (BF1 fiber pair), squares (BF2), and circles (BF3).

Fig. 11

Reduced scattering coefficient values (${\mu }_s^{\prime }$, ${\mathrm{mm}}^{-1}$) as a function of Intralipid concentrations.

Fig. 12

Permutation test to check for overtraining with unnormalized spectral features. Samples were randomly shuffled 100 times. The mean and standard deviation of the AUC obtained from the shuffling are presented and compared to the real AUCs. Note that analyses with this unnormalized spectral dataset failed to achieve statistical significance in binary classification tasks of benign versus MD and benign versus SD.