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.

Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts

“…The μ a was calculated by measuring a diluted solution of teal India ink in distilled water using a spectrophotometer (5102-00, PerkinElmer) and the Beer-Lambert Law. [18][19][20]26 A 5 × 3 (15 total) set of calibration phantoms was created, corresponding to five scattering ranges and three absorbing ranges ( Fig. 3).…”

“…Then, the LUT was used as an inverse model to fit measured spectral data and extract optical properties. [18][19][20] DRS data at each SDS represent a weighted average of physiological parameters collected at increasing depths. Therefore, a one-layer inverse experimental model was chosen to quantity volume-averaged, rather than layerspecific, physiological parameters without assuming precise thickness of overlying skin layers.…”

Intra-class variability in diffuse reflectance spectroscopy: application to porcine adipose tissue

“…This variability is due to anatomical differences in the sample, instrument variation and/or slight variations in the position of the sample with respect to the spectroscopic system. This effect has been experimentally proved [11,12]. An adequate quantification and statement of a non-random character of the magnitude of this spectral variability is essential for any optical diagnostic technique based on DRS.…”

Spatially resolved diffuse reflectance spectroscopy endoscopic sensing with custom Si photodetectors

“…The utility of SRDRS for quantitative optical characterization of tissues has been widely recognized [18][19][20][21][22]. The increased dimensionality of the DRS data yields increased information density for the unique determination of tissue optical properties, and the illumination/detection separations of SRDRS probes may be optimized for specific tissue applications to reduce noise and target specific interrogation depths [23,24].…”

“…The increased dimensionality of the DRS data yields increased information density for the unique determination of tissue optical properties, and the illumination/detection separations of SRDRS probes may be optimized for specific tissue applications to reduce noise and target specific interrogation depths [23,24]. These advantages have motivated several investigations into the endoscopic implementation of SRDRS for characterization of GI screening for dysplasia and cancer using fiber bundle probes for in-vivo characterization of stomach tissues [10], colon tissues [11], and oral tissues [22]. However, fiber bundle probes have several disadvantages for SRDRS, including low collection efficiency due to low numerical aperture (NA), low fill factor, and limited geometrical collection configurations (typically round) [25,26].…”