Towards monitoring dysplastic progression in the oral cavity using a hybrid fiber-bundle imaging and spectroscopy probe

Abstract

Intraepithelial dysplasia of the oral mucosa typically originates in the proliferative cell layer at the basement membrane and extends to the upper epithelial layers as the disease progresses. Detection of malignancies typically occurs upon visual inspection by non-specialists at a late-stage. In this manuscript, we validate a quantitative hybrid imaging and spectroscopy microendoscope to monitor dysplastic progression within the oral cavity microenvironment in a phantom and pre-clinical study. We use an empirical model to quantify optical properties and sampling depth from sub-diffuse reflectance spectra (450-750 nm) at two source-detector separations (374 and 730 μm). Average errors in recovering reduced scattering (5-26 cm(-1)) and absorption coefficients (0-10 cm(-1)) in hemoglobin-based phantoms were approximately 2% and 6%, respectively. Next, a 300 μm-thick phantom tumor model was used to validate the probe's ability to monitor progression of a proliferating optical heterogeneity. Finally, the technique was demonstrated on 13 healthy volunteers and volume-averaged optical coefficients, scattering exponent, hemoglobin concentration, oxygen saturation, and sampling depth are presented alongside a high-resolution microendoscopy image of oral mucosa from one volunteer. This multimodal microendoscopy approach encompasses both structural and spectroscopic reporters of perfusion within the tissue microenvironment and can potentially be used to monitor tumor response to therapy.

Figure 1

Representation of the hybrid fiber-bundle imaging and spectroscopy system showing (a) the major instrumentation components including (from left to right) fiber switch, imaging portion, and spectroscopy portion, (b) a SolidWorks representation of the distal probe (scale bar = 1 cm) showing the (c) en face view of the central 1 mm-diameter image fiber and 5 surrounding 200 μm multimode fibers (scale bar = 2.5 mm), (d) distal probe (scale bar = 1 cm), and (e) en face view of the distal probe tip (scale bar = 2.5 mm).

Figure 2

Comparison of the optical properties of the (a,b) 6 × 2 (12 total) calibration phantoms (C.P.) and the (c,d) 3 × 3 (9 total) validation phantoms (V.P.). Calibration phantoms were made with polystyrene microspheres and a combination of yellow, red, and blue dye and the validation phantoms were made with polystyrene microspheres and bovine hemoglobin as the scattering and absorbing agents, respectively. Calibration phantoms had μ_s′ spanning 3–31 cm⁻¹ and μ_a spanning 0–47 cm⁻¹ and the validation phantoms had a μ_s′ spanning 5–26 cm⁻¹ and μ_a spanning 0–10 cm⁻¹ to validate the target LUT range.

Figure 3

The probe is placed (a) in contact with the highly absorbing (μ_a ≥ 100 cm⁻¹ for 450–750 nm) inside a 5 mL beaker and translated upwards in 50 μm increments to (b) acquire sDRS data from a calibration phantom (C.P. 11) at a 374 μm SDS. (c) Representative data from the 374 μm SDS shows the percentage of photons not reaching the highly absorbing layer as a function of depth for C.P. 11 at 585 nm. Sampling depth is defined as the depth reached by 50% of photons.

Figure 4

A simplified representation of dysplastic proliferation arising at the basement membrane in the oral cavity (a–c) showing normal cells (gray with nuclei), dysplastic cells (light gray with nuclei), basement membrane (dark gray), and the stroma (gray). The associated dysplasia-mimicking phantom models (d–f) simulate this progression. Two SDSs (374 and 730 μm) deliver and collect broadband light at different depths (detected photons shown here as blue and red crescents, respectively). Each of thin phantom layers was 150 μm thick for a total phantom thickness of 300 μm to simulate the thickness of oral epithelium.

Figure 5

An image of the experimental setup showing the optical instrumentation, post-processing software based in MATLAB showing a high-resolution fluorescence image of the inner lip, LUT-based inverse model fit of raw reflectance data, sampling depth, μ_s′, and μ_a from one volunteer (image center), and the proximal and distal hybrid fiber-bundle probe.

Figure 6

100% (μ_s′ = 5–26 cm⁻¹, μ_a = 0–10 cm⁻¹) of both reflectance LUTs were validated with acceptable percent errors less than 10%. Following validation, optical properties can be reliably extracted from samples with unknown optical properties using the LUT-based inverse model. (a,b) Reflectance LUTs were interpolated with raw data from calibration phantoms and (c) shows a ratio of the 730 μm SDS to 374 μm SDS LUTs. (d,e) Reflectance LUTs were validated with raw data from the bovine hemoglobin-based validation phantoms and (f) shows the validated ratio of the 730 μm SDS to 374 μm SDS LUTs.

Figure 7

The LUT-based inverse model correctly estimated μ_s′ with average percent errors of 1.6% and 2.5% for the 374 and 730 μm SDS, respectively, and correctly estimated μ_a with average percent errors of 4.2% and 7.2% for the 374 and 730 μm SDS, respectively. The ability to extract optical properties is shown with a perfect fit line.

Figure 8

100% (μ_s′ = 5–26 cm⁻¹, μ_a = 0–10 cm⁻¹) of both sampling depth LUTs were validated with acceptable percent errors much less than 10%. (a,b) Sampling depth LUTs were interpolated with raw data from calibration phantoms and (c) shows a ratio (1.2×) of the 730 μm SDS to 374 μm SDS sampling depths. (d,e) Sampling depths LUTs were validated with raw data from the bovine hemoglobin-based validation phantoms and (f) shows the validated ratio of the 730 μm SDS to 374 μm SDS sampling depths.

Figure 9

The volume-averaged μ_s′ (a,b) increased as the proliferating scattering heterogeneity moved upwards towards the phantom surface (going from P1 to P3) showing a vertical line at 630 nm, in which percent increase in volume-averaged μ_s′ was measured from. There was a significantly greater μ_s′ increase in these values for the 374 μm SDS compared to the 730 μm SDS, indicating that the shorter SDS is more sensitive to superficial scattering changes associated with early epithelial dysplasia.

Figure 10

Comparison of qualitative and quantitative data acquired by the hybrid imaging and spectroscopy technique from 13 healthy volunteers showing (a) a high-resolution fluorescence image of apical oral mucosa from the inner lip of one volunteer (scale bar = 200 μm), (b) representative absolute reflectance profiles showing reflectance data and the overlaid LUT-based inverse model fits from the same volunteer from (a,c) average sampling depths for each SDS, (d) scattering exponent (B), (e) hemoglobin concentration ([Hb]), and (f) oxygen saturation (SaO₂). Error bars from (c–f) represent standard deviation.