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

Diffuse Reflectance Spectroscopy of the Oral Mucosa: In Vivo Experimental Validation of the Precancerous Lesions Early Detection Possibility

This article is devoted to the experimental validation of the possibility of early detection of precancerous lesions in the oral mucosa in vivo using diffuse reflectance spectroscopy in the wavelength range from 360 to 1000 nm. During the study, a sample of 119 patients with precancerous lesions has been collected and analyzed. As a result of the analysis, the most informative wavelength ranges were determined, in which the maximum differences in the backscattering spectra of lesions and intact tissues were observed, methods for automatic classification of backscattering spectra of the oral mucosa were studied, sensitivity and specificity values, achievable using diffuse reflectance spectroscopy for detecting hyperkeratosis on the tongue ventrolateral mucosa surface and buccal mucosa, were evaluated. As a result of preliminary experimental studies in vivo, the possibility of automatic detection of precancerous lesions of the oral mucosa surface using diffuse reflectance spectroscopy in the wavelength range from 500 to 900 nm with an accuracy of at least 75 percent has been shown.

Label-Free Optical Spectroscopy for Early Detection of Oral Cancer

Oral cancer is the 16th most common cancer worldwide. It commonly arises from painless white or red plaques within the oral cavity. Clinical outcome is highly related to the stage when diagnosed. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Therefore, there is a need to develop a non-invasive cost-effective diagnostic technique to differentiate non-malignant and early-stage malignant lesions. Optical spectroscopy may provide an appropriate solution to facilitate early detection of these lesions. It has many advantages over traditional approaches including cost, speed, objectivity, sensitivity, painlessness, and ease-of use in clinical setting for real-time diagnosis. This review consists of a comprehensive overview of optical spectroscopy for oral cancer diagnosis, epidemiology, and recent improvements in this field for diagnostic purposes. It summarizes major developments in label-free optical spectroscopy, including Raman, fluorescence, and diffuse reflectance spectroscopy during recent years. Among the wide range of optical techniques available, we chose these three for this review because they have the ability to provide biochemical information and show great potential for real-time deep-tissue point-based in vivo analysis. This review also highlights the importance of saliva-based potential biomarkers for non-invasive early-stage diagnosis. It concludes with the discussion on the scope of development and future demands from a clinical point of view.

Application of Classification Algorithms to Diffuse Reflectance Spectroscopy Measurements for Ex Vivo Characterization of Biological Tissues

Biological tissue identification in real clinical scenarios is a relevant and unsolved medical problem, particularly in the operating room. Although it could be thought that healthy tissue identification is an immediate task, in practice there are several clinical situations that greatly impede this process. For instance, it could be challenging in open surgery in complex areas, such as the neck, where different structures are quite close together, with bleeding and other artifacts affecting visual inspection. Solving this issue requires, on one hand, a high contrast noninvasive technique and, on the other hand, powerful classification algorithms. Regarding the technique, optical diffuse reflectance spectroscopy has demonstrated such capabilities in the discrimination of tumoral and healthy biological tissues. The complex signals obtained, in the form of spectra, need to be adequately computed in order to extract relevant information for discrimination. As usual, accurate discrimination relies on massive measurements, some of which serve as training sets for the classification algorithms. In this work, diffuse reflectance spectroscopy is proposed, implemented, and tested as a potential technique for healthy tissue discrimination. A specific setup is built and spectral measurements on several ex vivo porcine tissues are obtained. The massive data obtained are then analyzed for classification purposes. First of all, considerations about normalization, detrending and noise are taken into account. Dimensionality reduction and tendencies extraction are also considered. Featured spectral characteristics, principal component or linear discrimination analysis are applied, as long as classification approaches based on k-nearest neighbors (k-NN), quadratic discrimination analysis (QDA) or Naïve Bayes (NB). Relevant parameters about classification accuracy are obtained and compared, including ANOVA tests. The results show promising values of specificity and sensitivity of the technique for some classification algorithms, even over 95%, which could be relevant for clinical applications in the operating room.

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

Diffuse reflectance spectroscopy (DRS) is a probe-based spectral biopsy technique used in cancer studies to quantify tissue reduced scattering (μs') and absorption (μa) coefficients and vary in source-detector separation (SDS) to fine-tune sampling depth. In subcutaneous murine tumor allografts or xenografts, a key design requirement is ensuring that the source light interrogates past the skin layer into the tumor without significantly sacrificing signal-to-noise ratio (target of ≥15  dB). To resolve this requirement, a DRS probe was designed with four SDSs (0.75, 2.00, 3.00, and 4.00 mm) to interrogate increasing tissue volumes between 450 and 900 nm. The goal was to quantify percent errors in extracting μa and μs', and to quantify sampling depth into subcutaneous Balb/c-CT26 colon tumor allografts. Using an optical phantom-based experimental method, lookup-tables were constructed relating μa,μs', diffuse reflectance, and sampling depth. Percent errors were <10  %   and 5% for extracting μa and μs', respectively, for all SDSs. Sampling depth reached up to 1.6 mm at the first Q-band of hemoglobin at 542 nm, the key spectral region for quantifying tissue oxyhemoglobin concentration. This work shows that the DRS probe can accurately extract optical properties and the resultant physiological parameters such as total hemoglobin concentration and tissue oxygen saturation, from sufficient depth within subcutaneous Balb/c-CT26 colon tumor allografts. Methods described here can be generalized for other murine tumor models. Future work will explore the feasibility of the DRS in quantifying volumetric tumor perfusion in response to anticancer therapies.

Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks

Subdiffusive reflectance captured at short source-detector separations provides increased sensitivity to the scattering phase function and hence allows superficial probing of the tissue ultrastructure. Consequently, estimation of subdiffusive optical parameters has been the subject of many recent studies focusing on lookup-table-based (LUT) inverse models. Since an adequate description of the subdiffusive reflectance requires additional scattering phase function related optical parameters, the LUT inverse models, which grow exponentially with the number of estimated parameters, become excessively large and computationally inefficient. Herein, we propose, to the best of our knowledge, the first artificial-neural-network-based inverse Monte Carlo model that overcomes the limitations of the LUT inverse models and thus allows efficient real-time estimation of optical parameters from subdiffusive spatially resolved reflectance. The proposed inverse model retains the accuracy, is about four orders of magnitude faster than the LUT inverse models, grows only linearly with the number of estimated optical parameters, and can be easily extended to estimate additional optical parameters.

Effects of isoflurane anesthesia on physiological parameters in murine subcutaneous tumor allografts measured via diffuse reflectance spectroscopy

Diffuse reflectance spectroscopy (DRS) has been used in murine studies to quantify tumor perfusion and therapeutic response. These studies frequently use inhaled isoflurane anesthesia, which depresses the respiration rate and results in the desaturation of arterial oxygen saturation, potentially affecting tissue physiological parameters. However, there have been no controlled studies quantifying the effect of isoflurane anesthesia on DRS-derived physiological parameters of murine tissue. The goal of this study was to perform DRS on Balb/c mouse (n = 10) tissue under various anesthesia conditions to quantify effects on tissue physiological parameters, including total hemoglobin concentration, tissue oxygen saturation, oxyhemoglobin and reduced scattering coefficient. Two independent variables were manipulated including metabolic gas type (pure oxygen vs. medical air) and isoflurane concentration (1.5 to 4.0%). The 1.5% isoflurane and 1 L/min oxygen condition most closely mimicked a no-anesthesia condition with oxyhemoglobin concentration within 89% ± 19% of control. The time-dependent effects of isoflurane anesthesia were tested, revealing that anesthetic induction with 4.0% isoflurane can affect DRS-derived physiological parameters up to 20 minutes post-induction. Finally, spectroscopy with and without isoflurane anesthesia was compared for colon tumor Balb/c-CT26 allografts (n = 5) as a representative model of subcutaneous murine tumor allografts. Overall, isoflurane anesthesia yielded experimentally-induced depressed oxyhemoglobin, and this depression was both concentration and time dependent. Investigators should understand the dynamic effects of isoflurane on tissue physiological parameters measured by DRS. These results may guide investigators in eliminating, limiting, or managing anesthesia-induced physiological changes in DRS studies in mouse models.

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

Optical diffuse reflectance spectroscopy (DRS) has great potential in the study, diagnosis, and discrimination of biological tissues. Discrimination is based on massive measurements that conform training sets. These sets are then used to classify tissues according to the biomedical application. Classification accuracy depends strongly on the training dataset, which typically comes from different samples of the same class, and from different points of the same sample. The variability of these measurements is not usually considered and is assumed to be purely random, although it could greatly influence the results. In this work, spectral variations within and between samples of different animals of ex-vivo porcine adipose tissue are evaluated. Algorithms for normalization, dimensionality reduction by principal component analysis, and variability control are applied. The PC analysis shows the dataset variability, even when a variability removal algorithm is applied. The projected data appear grouped by animal in the PC space. Mahalanobis distance is calculated for every group, and an ANOVA test is performed in order to estimate the variability. The results confirm that the variability is not random and is dependent at least on the anatomical location and the specific animal. The variability magnitude is significant, particularly if the classification accuracy is needed to be high. As a consequence, it should be taken generally into account in classification problems.

Spatially resolved diffuse reflectance spectroscopy endoscopic sensing with custom Si photodetectors

Early detection and surveillance of disease progression in epithelial tissue is key to improving long term patient outcomes for colon and esophageal cancers, which account for nearly a quarter of cancer related mortalities worldwide. Spatially resolved diffuse reflectance spectroscopy (SRDRS) is a non-invasive optical technique to sense biological changes at the cellular and sub-cellular level that occur when normal tissue becomes diseased, and has the potential to significantly improve the current standard of care for endoscopic gastrointestinal (GI) screening. Herein the design, fabrication, and characterization of the first custom SRDRS device to enable endoscopic SRDRS GI tissue characterization using a custom silicon (Si) thin film multi-pixel endoscopic optical sensor (MEOS) is described.

Objective identification of dental abnormalities with multispectral fluorescence imaging

Sensitive methods that can enable early detection of dental diseases (caries and calculus) are desirable in clinical practice. Optical spectroscopic approaches have emerged as promising alternatives owing to their wealth of molecular information and lack of sample preparation requirements. In the present study, using multispectral fluorescence imaging, we have demonstrated that dental caries and calculus can be objectively identified on extracted tooth. Spectral differences among control, carious and calculus conditions were attributed to the porphyrin pigment content, which is a byproduct of bacterial metabolism. Spectral maps generated using different porphyrin bands offer important clues to the spread of bacterial infection. Statistically significant differences utilizing fluorescence intensity ratios were observed among three groups. In contrast to laser induced fluorescence, these methods can provide information about exact spread of the infection and may aid in long term dental monitoring. Successful adoption of this approach for routine clinical usage can assist dentists in implementing timely remedial measures.