Abstract

We report on the development of a unique real-time calibrating polarization-sensitive diffuse reflectance (rcPS-DR) handheld probe, and demonstrate its diagnostic potential through in-depth characterization and differentiation of clinically relevant anatomical locations of the oral cavity (i.e., alveolar process, lateral tongue and floor of mouth that account for 80% of all cases of oral squamous cell carcinoma) in vivo. With an embedded calibrating polytetrafluoroethylene (PTFE) optical diffuser, the PS-DR spectra bias arising from instrument response, time-dependent intensity fluctuation and fiber bending is calibrated through real-time measurement of the PS-DR system response function. A total of 554 in vivo rcPS-DR spectra were acquired from different oral tissue sites (alveolar process, n = 226, lateral tongue, n = 150 and floor of mouth, n = 178) of 14 normal subjects. Significantly (P<0.05, unpaired 2-sided Student’s t-test) different spectral ratio (I540/I575) representing oxygenated hemoglobin contents were found among the alveolar process, lateral tongue and floor of mouth. Further partial least squares discriminant analysis (PLS-DA) and leave-one-out, cross validation (LOOCV) show that, synergizing the complementary information of the two real-time calibrated orthogonal-polarized PS-DR spectra, the rcPS-DR technique is found to better differentiate alveolar process, lateral tongue, and the floor of mouth (accuracies of 88.2%, 83.9%, 84.4%, sensitivities of 80.5%, 75.8%, 78% and specificities of 93.5%, 87.7%, 86.8%) than standard DR (accuracies of 80.8%, 72.9%, 68.5%, sensitivities of 63.2%, 41.5%, 81.3% and specificities of 92.9%, 87.7%, 63.8%) without PS detection. This work showed the feasibility of the rcPS-DR probe as a tool for studying oral cavity lesions in real clinical applications.

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

With an annual incidence of 377 713 cases and 177 757 deaths, oral cancer is one most common malignancy worldwide [1]. Patients diagnosed with advanced stage oral cancer have a 5-year survival rate of only 30%, while the patient’s survival can be improved up to 80% to 90% if the oral cancer can be detected and diagnosed early for appropriate treatments [2]. However, current routine screening of oral cancer relaying on medical, social and familial history documentation, risk factor (i.e., tobacco and alcohol usage) evaluation, visual inspection and palpation were deemed insufficient to detect oral cancer early. Invasive random biopsy followed by H&E histopathology are therefore recommended and remain the gold standard for oral cancer detection, but found with limitations. For instance, the H&E slide preparation may distort the oral tissue features, whilst the slide interpretation is highly subjective and depends heavily on the experiences of the pathologists. In particular, the biopsy strategy may not be suitable for patients having multiple suspicious lesions. Therefore, there exists unmet clinical need to develop more advanced diagnostic techniques for rapid, objective and enhanced diagnosis of cancer and early cancer in the oral cavity.

Several techniques have been developed and implemented to fill the unmet need, including the established imaging modalities (e.g., computed tomography, magnetic resonance imaging, and positron emission tomography etc. [3]), ultrasound imaging [4], together with a range of different optical imaging/spectroscopy techniques (e.g., OCT: optical coherence tomography [5], photoacoustic tomography [6], hyperspectral imaging [7], auto-fluorescence spectroscopy [8], Raman spectroscopy [9], diffuse reflectance (DR) spectroscopy [10] etc.) under development. Compared with established imaging modalities and/or ultrasound imaging, the optics based oral cancer detection techniques are found with multi advantages. First, optics-based techniques reduce patient exposure to harmful radiation. Second, they offer high resolution (on the order of micrometers or/and sub-micrometers). Third, they are good at visualizing soft tissues [11].

Among the various optics-based techniques developed for cancer detection, DR spectroscopy analyzing the sample optical (including scattering and absorption coefficients [12]), biochemical (water, lipid, protein, glucose [13], hemoglobin and blood oxygen concentration changes [14], etc.) properties and the changes thereof is gaining popularity for tissue characteristion in a number of organs including oral cavity [1518]. In particular, polarization-sensitive (PS) DR spectroscopy, including that is used in combination with Mie theory calculations to further extract morphological information about epithelial tissue [19,20], that is used to enable depth-selective DR spectroscopy [21], that is used to extract full polarization properties (i.e., depolarization, diattenuation, and retardation et.al.) [22,23] and that is used with machine learning for detection of skin complications caused by diabetes [24], show promise for enhancing the performance of standard DR spectroscopy (without PS detection). Routine application of DR spectroscopy requires only one-time calibration to compensate for lamp intensity fluctuations, wavelength-dependent instrument response, inter-device variations, and fiber bending losses. However, clinical DR spectroscopy utilizes optical fiber that will be unavoidably and frequently twisted by the clinicians, resulting in changing DR system-response and introducing DR spectra bias. To tackle this challenge, Bing Yu et. al. [2528] innovates a ‘self-calibrating’ DR probe capable of measuring and calibrating the DR spectroscopy system-response in real-time, resulting in better consistency among DR spectra measured across variant DR spectroscopy systems. In addition, the reported PS-DR probe is inherently capable of generating system-response independent spectroscopic ‘depolarization ratio’ [20]. Nevertheless, the two raw orthogonal polarized DR spectra are not system independent. To maximize the diagnostic advantages of PS-DR spectroscopy [23], system-response independent PS-DR spectra carrying more comprehensive diagnostic information are highly needed. To fill the need, we report, in this work, on the development of a unique real-time calibrating PS-DR (rcPS-DR) handheld probe. With an embedded calibrating polytetrafluoroethylene (PTFE) optical diffuser, the PS-DR spectra bias arising from instrument response and time-dependent intensity fluctuation is removed through real-time measurement of the PS-DR system-response function. The diagnostic advantages of rcPS-DR probe are confirmed through its enhanced differentiation of the three clinically relevant anatomical locations (i.e., alveolar process, lateral tongue and floor of mouth that account for 80% of all cases of oral squamous cell carcinoma [29]) of the oral cavity.

2. Material and methods

2.1 Real-time calibrating polarization-sensitive diffuse reflectance spectroscopy (rcPS-DR) system

Figure 1 shows the schematic of the rcPS-DR system developed for tissue measurements. The rcPS-DR system consists of an LED source (Solis-3C, Thorlabs, NJ, USA) for illumination, customized spectrometer array for PS-DR spectra measurements, and a unique rcPS-DR handheld probe. The three spectrometers (SunShine, CNILaser, ChangChun, China) of the customized spectrometer array are of the same specs, resulting in minimum inter-spectrometer variation and resultant interferences on the PS-DR spectra measured. Further, the spectra acquisition of the three spectrometers were synchronized by an external trigger box (EX-TA-Box, CNILaser, ChangChun, China). The rcPS-DR probe features five multimode fibers (FG200LEA, Thorlabs, NJ, USA) for light delivery. Three of the fibers (Exc. Fiber, Det. Fiber ($ \| $), and Det. Fiber (⊥), as shown in Fig. 1) consisted of PS-DR spectra acquisition channel and the other two fibers (Calib. Exc. Fiber, Calib. Det. Fiber, as shown in Fig. 1) calibration channel used for real-time PS-DR spectra calibration. Along the PS-DR spectra acquisition channel, there exists paired and custom cut polarizing films (Polarizer ($ \| $), and Polarizer (⊥), as shown in Fig. 1. #86-178, EdmundOptics, NJ, USA) with their fast axes positioned orthogonal to each other, enabling polarization-sensitive DR excitation and collection. The excitation-collection fiber spacing of both channels were the same and kept as 1 mm, resulting in a PS-DR spectra interrogation diameter of 0.5 mm and a predicted (by Monte Carlo simulations [30]) interrogation depth ∼ 0.4-0.7 mm, as consistent with previous reports [17]. To enable real-time calibrated PS-DR spectra measurements, a 10 mm thick PTFE diffuser (Calib. PTFE, as shown in Fig. 1) was embedded in the calibration channel of the probe. The thickness (∼10 mm) of the diffuser was the same as the DR standard (WS-1 diffuse reflectance standard, Ocean Insight, FL, USA), enabling PS-DR system response approaching to that of the DR standard (as will be shown below). One notes that the polarization films incorporated are custom cut into 1 mm by 1 mm, and the relative positioning of the probe components (i.e., beam delivery fibers, polarization films, and PTFE diffuser, etc.) were ensured through a 3D-printed component (Boston Micro Fabrication, Shenzhen, China). The overall probe tip diameter is 8 mm.

 figure: Fig. 1.

Fig. 1. (a) Schematic of the rcPS-DR system developed for tissue measurements. (b) distal tip of the probe. Light emission diode (LED); spectrometer (Spec); excitation Fiber (Exc. Fiber); detection fiber (Det. Fiber); excitation fiber in the calibration channel (Calib. Exc. Fiber); detection fiber in the calibration channel (Calib. Det. Fiber); polytetrafluoroethylene for calibration (Calib. PTFE).

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To acquire the PS-DR spectra, light output from the LED is first coupled into the excitation fibers (Exc. Fiber, Calib. Exc. Fiber, as shown in Fig. 1) of both the acquisition and the calibration channel. On the one hand, the light input to the acquisition channel is further linearly-polarized (by Polarizer ($ \| $), as shown in Fig. 1) and shined onto the sample. Part of the backscattered DR spectra from the sample passed through the same polarizer enabling polarized illumination, whist the remaining backscattered DR spectra passed through the other orthogonally-polarized polarizer. All backscattered PS-DR spectra were sent to the spectrometer array through the detection fibers in the acquisition channel. On the other hand, the light input to the calibration channel was backscattered by the embedded PTFE diffuser and passed to the spectrometer array. One notes that since the PS-DR system response function is measured through the calibration channel of the rcPS-DR probe, in real-time and in synergy with the PS-DR spectra, the PS-DR spectra bias arising from instrument response, time-dependent LED intensity fluctuation and fiber bending can be removed (as will be shown below).

To quantify the polarization property of the sample under investigation, the spectroscopic degree of linear polarization (DLP) was further extracted from the two orthogonal-polarized and real-time calibrated PS-DR spectra as below [31]:

$$DLP = \; \frac{{{I_{||}}(\lambda )- {I_ \bot }(\lambda )}}{{{I_{||}}(\lambda )+ {I_ \bot }(\lambda )}}$$
where ${I_{||}}(\lambda )$ and ${I_ \bot }(\lambda )$ are the back-scattered PS-DR spectra with polarization parallel and perpendicular to the excitation light, respectively, and $\lambda $ represents the light wavelength.

2.2 Statistical analysis

The unpaired two-sided Student’s t-test was used to evaluate the rcPS-DR spectra differences among alveolar process, lateral tongue and floor of mouth [32]. A criterion of P value less than 0.05 was used to consider differences as statistically significant. Partial least squares (PLS) - discriminant analysis (DA) was applied on the rcPS-DR spectra to classify the different sites of the oral cavity [33]. Leave-one-out, cross-validation (LOOCV) was further used to assess and optimize the PLS-DA model complexity, while reducing the risk of over-fitting [33]. The adopted cross validation strategy was leave one tissue site out [33]. One notes that one-way analysis of variance (ANOVA) with a Fisher post hoc least significant difference (LSD) test was used to evaluate which wavelength range of the PS-DR spectra contributes most to the PLS-DA analysis before PLS-DA model development [34]. The above multivariate statistical analysis was performed using both in-house written scripts and open-source PLS-DA tool [33] in the Matlab programming environment (Mathworks. Inc., Natick, MA

2.3 Subjects

A total of 14 normal healthy subjects (10 females and 4 males, median age of 26) were recruited for in vivo rcPS-DR spectra measurements from the oral cavity. Informed consent forms were obtained from all participating subjects. Exclusion criteria included smokers, regular alcohol consumers and subjects suffering from systemic or oral mucosal diseases. Before in vivo rcPS-DR spectra measurements, all subjects underwent extensive mouthwash to reduce confounding factors (eg, food debris, microbial coatings). A total of 6 anatomic locations (i.e., left and right sides of alveolar process: left and right sides of lateral tongue; left and right sides of the floor of mouth, as illustrated in https://visualsonline.cancer.gov/details.cfm?imageid=9259) were predefined for rcPS-DR spectra measurements; and a total of 554 in vivo rcPS-DR spectra (alveolar process, n = 226, lateral tongue, n = 150 and floor of mouth, n = 178, as summarized in Table 1) were acquired from different oral tissue sites (alveolar process, n = 84, lateral tongue, n = 70 and floor of mouth, n = 84) of the recruited subjects.

Tables Icon

Table 1. The detailed tissue types break down and sample distribution. AP: alveolar process. FM: floor of mouth; LT: lateral tongue.

3. Results and discussion

3.1 Real-time calibrating capability evaluation of the rcPS-DR probe

To evaluate the real-time calibrating capability of the rcPS-DR probe, the system response functions (SRFs) determined by the rcPS-DR embedded diffuser and the DR standard (WS-1 diffuse reflectance standard, Ocean Insight, FL, USA) were measured and compared (Fig. 2(a)). As shown in Fig. 2(a), both SRFs were consistent and close to each other, suggesting that the rcPS-DR probe is capable of measuring the PS-DR SRF accurately. Further, we mimicked the fiber bending that is usually encountered and unavoidable during the clinical applications of the fiber-optic DR spectroscopy system. Using mirror (PF10-03-G01, Thorlabs, NJ, USA) positioned 20 µm away from the rcPS-DR probe as sample, different fiber bending radius were tested (5, 10, 15, 20 mm, Fig. 2(b)) with corresponding SRFs measured, demonstrating the different SRFs that would otherwise be neglected by the generally adopted one-time calibration strategy. Figure 2(c) shows the calibrated and therefore consistent PS-DR spectra in the detection channel, suggesting the real-time calibrating capability of the rcPS-DR probe developed. We also calculated the DLP (degree of linear polarization) when using the mirror (PF10-03-G01, Thorlabs, NJ, USA) as sample (Fig. 2(d)). We found the DLP measured deviates from but close to 1, validating the polarization detection capability of the rcPS-DR probe. One notes that the deviation is likely caused by the imperfection of the linear polarizer films used (Polarizer ($ \| $), Polarizer (⊥), as shown in Fig. 1).

 figure: Fig. 2.

Fig. 2. (a) PS-DR system response function measured by both the DR standard and the rcPS-DR embedded diffuser. (b) rcPS-DR system response function changes under different fiber bending radiuses. (c) The rcPS-DR spectra under the same fiber bending conditions as in (b). (d) The calculated DLP (degree of linear polarization). Note: the results in (c, d) both uses flat reflection mirror (PF10-03-G01, Thorlabs, NJ, USA) as sample.

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In a separate experiment, we further investigate how the rcPS-DR probe functions for real-time calibration of the PS-DR SRFs. The intensity variations of the LED source were simulated through gradual reduction of the LED intensity through driving current adjustment using the LED driver (DC20, Thorlabs, NJ, USA). The raw PS-DR spectra in both polarization detection channels were measured (Figs. 3 a-b) in concurrence with the SRF of the calibration channel. While the raw PS-DR spectra were found with intensity variations arising from the LED intensity variations, the calibrated PS-DR spectra were shown (Figs. 3(c-d)) with intensity variation less than ± 5%, confirming the real-time calibrating capability of the rcPS-DR probe developed. The remaining ± 5% variation could be attributed to the variant coupling between the LED source and the rcPS-DR probe when tuning the LED intensity. The DLP measured was calculated to be close to 1 (Fig. 3(e)), revalidating the polarization detection capability of the rcPS-DR probe.

 figure: Fig. 3.

Fig. 3. (a, b) Raw PS-DR spectra reflected by the mirror under different levels of LED illumination of parallel (a, $ \| $), and perpendicular (b, ⊥) polarization detection channels, before calibration. (c, d) PS-DR spectra after calibration (i.e., ratio of the mirror-reflected intensity and the real-time calibrated PS-DR system response function). (e) DLP. The different mirror-reflected intensities in (a-b) are generated through adjustment of the LED driver current (10A, 6A, 3A, and 1.5A).

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3.2 Polarization functionality validation of the rcPS-DR probe

Whilst the results in Figs. 23 confirm the real-time calibrating performance and validate the polarization detection capability of the rcPS-DR probe, further experiment was conducted to determine the polarization functionality of the rcPS-DR probe. External polarized light was launched into the rcPS-DR probe. As shown in Fig. 4(a), light delivered from an external multimode fiber was firstly collimated, linear polarized, and then was incident onto the rcPS-DR probe after passing through a half waveplate (HWP, AHWP20-VIS, LBTEK, Shenzhen, China). The HWP was rotated from 0 to 360 degrees, changing the polarization by 360 degrees. According to Malus Law, the intensities of HWP-tuned polarized light (${I_0}(\lambda )$) that passes through the acquisition channel of the rcPS-DR probe (${I_{||}}(\lambda )$ and ${I_ \bot }(\lambda )$) vary as the square of the cosine of the angle between the HWP fast axis and those of both polarizer films (ϕ and ϕ + π/2) within rcPS-DR probe, i.e., ${I_{||}}(\lambda )\; $=${I_0}(\lambda )\textrm{cos}{(\phi )^2}$, and ${I_ \bot }(\lambda )\; $=${I_0}(\lambda )\textrm{cos}{({\phi + \mathrm{\pi }/2} )^2}$. Besides, the DLP changes were calculated to be: $\textrm{cos}{(\phi )^2} - \; \textrm{sin}{(\phi )^2}$. The experimentally collected PS-DR spectra in both polarization acquisition channels (Figs. 4 b-d) and the relevant DLP (Fig. 4(e)) versus the HWP rotation angles were also shown, which were consistent with the predicted intensities by Malus Law. The results in Fig. 4 confirm the polarization detection capability of the rcPS-DR probe developed.

 figure: Fig. 4.

Fig. 4. (a) Schematic setup to determine the polarization detection capability of the rcPS-DR probe with external polarized light. (b, c) Raw PS-DR spectra for parallel (b, $ \| $), and perpendicular (c, ⊥) polarization detection channels, under different (0 to 360 degrees) rotation angles of the HWP as in (a). (d) Typical PS-DR spectra changes versus HWP rotation angles, and the resultant DLP. The wavelength was selected at 575 nm of (d, e). Multimode fiber (MMF); Collimator (Col); Linear polarizer (LP); Half waveplate (HWP).

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3.3 Clinical performance evaluation of the rcPS-DR probe

In light of the above-confirmed real-time calibration (Figs. 23) and polarization detection (Fig. 4) capabilities of the rcPS-DR probe, we further sought to investigate the potential benefits of the rcPS-DR probe for real clinical applications. Figure 5(a) shows the real-time calibrated PS-DR spectra (mean ± SE, standard error) of alveolar process, lateral tongue and floor of mouth. The measured PS-DR spectra were consistent with previous studies [1517], showing clearly identified dips around 540 nm and 575 nm that could be attributed to oxygenated hemoglobin absorption. Further look into the PS-DR spectra reveals subtle width and amplitude differences of the dips among the three clinically relevant sites. Quantitatively, the intensity ratios of the two dips were also shown (Fig. 5(b)), demonstrating significant (P<0.05, unpaired 2-sided Student’s t-test) differences. In addition, the resulting DLP (Fig. 5(c)) differs among the three sites. We also found that the DLP (Fig. 5(c)) does not show a change corresponding to the dips of oxygenated hemoglobin absorption as in Fig. 5(a). This observation is consistent with previous reports [20], and warrants further investigations. The PS-DR spectra differences observed (Fig. 5) are likely caused by the significant structural differences among the alveolar process, lateral tongue and floor of mouth as revealed by our previous OCT study [35]. For instance, the alveolar processes investigated consists of ∼200 μm thick gingival layer on top of the underlying bone. The floor of mouth is comprised of clearly identified ∼240 μm thick non-keratinized epithelium above the lamina propria rich in collagen fibers, resulting in a significantly higher DLP (Fig. 5(c)) compared with the other two anatomical locations. Unlike the alveolar processes and floor of mouth, the lateral tongue is lacking of layering structure. However, how the structural differences cause the PS-DR spectra differences observed warrant further investigations. We are currently developing co-registered PS-DR spectroscopy and OCT imaging systems (i.e., PS-OCT and OCT angiography) to correlate and explain the PS-DR spectra findings of the current work.

 figure: Fig. 5.

Fig. 5. (a) Real-time calibrated PS-DR spectra (mean ± SE) measured from alveolar process, floor of mouth, and lateral tongues. (b). Box plot of the intensity ratios (mean ± SE) between I540 and I575 owing to oxygenated hemoglobin absorption. ○ Par and ◊ Perp correspond to the parallel- and perpendicular- polarization detection channels of the rcPS-DR probe. * (c) DLP (mean ± SE, standard error) of the alveolar process, floor of mouth, and lateral tongues. * P<0.05.

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To elucidate the diagnostically important PS-DR wavelength range, Fig. 6(a) shows a logarithmic plot of the calculated P-values (ANOVA Fisher post hoc LSD test at the 0.05 level.) for each of the PS-DR spectra intensities in the entire wavelength range. We find the PS-DR spectra over the entire wavelength range shows statistically different differences (P < 1e-4), PLS-DA and LOOCV were therefore implemented on the entire PS-DR spectra measured (Fig. 5(a)), allowing quantitative evaluation of the inter-anatomical PS-DR spectra differences of the oral cavity, Fig. 6(b) shows the 2-dimensional ternary plot of the posterior probabilities of each PS-DR prediction using PLSDA-LOSCV. The prediction results are also summarized in Table 2. Figure 6 and Table 2 elucidates that the 3 tissue sites can generally be well separated with varying sensitivities (alveolar process: 80.5%, floor of mouth: 75.8%, lateral tongue: 78%), and specificities (alveolar process: 93.5%, floor of mouth: 87.7%, lateral tongue:86.8%) (Table 2) by rcPS-DR, which are superior than those standard DR without PS detection (sensitivities of 63.2%, 41.5%, 81.3% and specificities of 92.9%, 87.7%, 63.8%). One notes that four lateral variables were used for the PLS-DA model with PS-DR spectra, while the number is three for that with DR spectra. The enhanced separation capability can be explained by the real-time calibration (Figs. 23) and polarization-sensitive detection (Fig. 4) capability of rcPS-DR probe developed. Overall, the results of this study indicate the potential of the rcPS-DR probe as a tool for studying oral cavity lesions in real clinical applications, and the PS-DR spectra variations of different oral tissue sites should be taken into account in algorithm development for accurate tissue diagnosis and characterization in the oral cavity by using rcPS-DR probe. We are currently seeking collaborations with clinicians to assess how the rcPS-DR probe developed could aid to differentiate oral malignant lesions from normal tissue and determine oral cavity tumor margins for surgical operations.

 figure: Fig. 6.

Fig. 6. (a) ANOVA of the three tissue categories over the entire PS-DR spectra wavelength. (b) posterior probabilities of 554 statistically different PS-DR spectra measured by the rcPS-DR handheld probe belonging to alveolar process (n = 226), floor of mouth (n = 178), and lateral tongue (n = 150).

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Tables Icon

Table 2. Confusion matrix detailing the multiclass classification results of PS-DR spectra of different oral tissues using PLS-DA and LOSCV. AP: alveolar process. FM: floor of mouth; LT: lateral tongue.

Some limitations of the current work should be pointed out. First, the DR spectroscopy is in general sensitive to the pressure applied, and stepwise changes could occur in tissue DR spectra even at subtle pressure [36]. When in use, the rcPS-DR probe developed is in gentle contact with the tissue sites measured, minimizing the pressure-introduced PS-DR spectra variation. However, for real clinical use of the rcPS-DR probe, further integration of a pressure sensor at the rcPS-DR probe tip is needed [27], allowing real-time pressure monitoring and assuring consistent pressure applied. Second, the current rcPS-DR probe is lacking depth-resolved DR spectra interrogation capability as required for epithelial precancer detection [37]. Pressure-sensitive depth-resolved rcPS-DR probe through integration of pressure sensor and focusing optics (i.e., ball lens, or GRIN lens) at the current rcPS-DR probe tip is under development within our lab.

4. Conclusion

In summary, we have developed a unique rcPS-DR probe. Significant different oxygenated hemoglobin contents were observed among different anatomic locations of the oral cavity (i.e., alveolar process, lateral tongue and the floor of mouth). Synergizing the complementary information of the two real-time calibrated orthogonal-polarized PS-DR spectra, the rcPS-DR probe is found to better differentiate alveolar process, lateral tongue, and the floor of mouth. This work demonstrates the potential of using rcPS-DR probe as a clinically useful tool for enhancing real-time in vivo detection and diagnosis of oral disease in the oral cavity.

Funding

Beijing Institute of Technology Research Fund Program for Young Scholars.

Disclosures

The authors declare no conflicts of interest.

Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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21. M. Jimenez, S. Lam, C. Poh, and K. Sokolov, “Depth sensitive oblique polarized reflectance spectroscopy of oral epithelial tissue,” Proc. SPIE 9155, 20 (2014). [CrossRef]  

22. R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized light-scattering spectroscopy,” Nat. Med. 7(11), 1245–1248 (2001). [CrossRef]  

23. J. Wang, W. Zheng, K. Lin, and Z. Huang, “Integrated Mueller-matrix near-infrared imaging and point-wise spectroscopy improves colonic cancer detection,” Biomed. Opt Express 7(4), 1116–1126 (2016). [CrossRef]  

24. V. Dremin, Z. Marcinkevics, E. Zherebtsov, A. Popov, A. Grabovskis, H. Kronberga, K. Geldnere, A. Doronin, I. Meglinski, and A. Bykov A, “Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning,” IEEE Trans. Med. Imaging 40(4), 1207–1216 (2021). [CrossRef]  

25. B. Yu B, H. Fu H, T. Bydlon, J. E. Bender, and N. Ramanujam, “Diffuse reflectance spectroscopy with a self-calibrating fiber optic probe,” Opt. Lett. 33(16), 1783–1785 (2008). [CrossRef]  

26. B. Yu, H. L. Fu, and N. Ramanujam, “Instrument independent diffuse reflectance spectroscopy,” J. Biomed. Opt. 16(1), 011010 (2011). [CrossRef]  

27. B. Yu, A. Shah, V. K. Nagarajan, and D. G. Ferris, “Diffuse reflectance spectroscopy of epithelial tissue with a smart fiber-optic probe,” Biomed. Opt. Express 5(3), 675–689 (2014). [CrossRef]  

28. V. K. Nagarajan, J. M. Ward, and B. Yu, “Association of liver tissue optical properties and thermal damage,” Lasers Surg. Med. 52(8), 779–787 (2020). [CrossRef]  

29. R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016). [CrossRef]  

30. J. Wang, M. S. Bergholt, W. Zheng, and Z. Huang, “Development of a beveled fiber-optic confocal Raman probe for enhancing in vivo epithelial tissue Raman measurements at endoscopy,” Opt. Lett. 38(13), 2321–2323 (2013). [CrossRef]  

31. V. Tuchin, “Polarized light interaction with tissues,” J. Biomed. Opt. 21(7), 071114 (2016). [CrossRef]  

32. J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Simultaneous fingerprint and high-wavenumber fiber-optic Raman spectroscopy improves in vivo diagnosis of esophageal squamous cell carcinoma at endoscopy,” Sci. Rep. 5(1), 12957 (2015). [CrossRef]  

33. Y. V. Zontov, O. Y. Rodionova, S. V. Kucheryavskiy, and A. L. Pomerantsev, “PLS-DA –a MATLAB GUI tool for hard and soft approaches to partial least squares discriminant analysis,” Chemometrics and Intelligent Laboratory Systems 203, 104064 (2020). [CrossRef]  

34. J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Fiber-optic Raman spectroscopy for in vivo diagnosis of gastric dysplasia,” Faraday Discuss. 187, 377–392 (2016). [CrossRef]  

35. J. Wang J, W. Zheng W, K. Lin K, and Z. Huang, “Characterizing biochemical and morphological variations of clinically relevant anatomical locations of oral tissue in vivo with hybrid Raman spectroscopy and optical coherence tomography technique,” J. Biophotonics. 11(3), e201700113 (2018). [CrossRef]  

36. A. Popov, A. Bykov, and I. Meglinski, “Influence of probe pressure on diffuse reflectance spectra of human skin measured in vivo,” J. Biomed. Opt. 11, 110504 (2017). [CrossRef]  

37. R. A. Schwarz, D. Arifler, S. K. Chang, I. Pavlova, I. A. Hussain, V. Mack, B. Knight, R. Richards-Kortum, and A. M. Gillenwater, “Ball lens coupled fiber-optic probe for depth-resolved spectroscopy of epithelial tissue,” Opt. Lett. 30(10), 1159–1161 (2005). [CrossRef]  

References

  • View by:

  1. H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA A Cancer J Clin. 71(3), 209–249 (2021).
    [Crossref]
  2. S. Tiziani, V. Lopes, and U. L. Günther, “Early stage diagnosis of oral cancer using 1H NMR-based metabolomics,” Neoplasia 11(3), 269–IN10 (2009).
    [Crossref]
  3. P. Pałasz, Ł. Adamski, M. Górska-Chrząstek, A. Starzyńska, and M. Studniarek, “Contemporary diagnostic imaging of oral squamous cell carcinoma - a review of literature,” Pol J Radiol 82, 193–202 (2017).
    [Crossref]
  4. T. J. W. Klein Nulent, R. Noorlag, E. M. Van Cann, F. A. Pameijer, S. M. Willems, A. Yesuratnam, A. Rosenberg, R. de Bree, and R. J. J. van Es, “Intraoral ultrasonography to measure tumor thickness of oral cancer: A systematic review and meta-analysis,” Oral Oncol. 77, 29–36 (2018).
    [Crossref]
  5. C. K. Lee, T. T. Chi, C. T. Wu, M. T. Tsai, C. P. Chiang, and C. C. Yang, “Diagnosis of oral precancer with optical coherence tomography,” Biomed. Opt. Express 3(7), 1632–1646 (2012).
    [Crossref]
  6. W. Qin, W. Qi, T. Jin, H. Guo, and L. Xi, “In vivo oral imaging with integrated portable photoacoustic microscopy and optical coherence tomography,” Appl. Phys. Lett. 111(26), 263704 (2017).
    [Crossref]
  7. H. Martin, V. L. James, W. Xu, Y. C. Amy, and F. Baowei, “Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks,” J. Biomed. Opt. 24, 1–9 (2019).
    [Crossref]
  8. C. Y. Wang, H. K. Chiang, C. T. Chen, C. P. Chiang, Y. S. Kuo, and S. N. Chow, “Diagnosis of oral cancer by light-induced autofluorescence spectroscopy using double excitation wavelengths,” Oral Oncol. 35(2), 144–150 (1999).
    [Crossref]
  9. E. M. Barroso, R. W. H. Smits, T. C. Bakker Schut, I. ten Hove, J. A. Hardillo, E. B. Wolvius, R. J. Baatenburg de Jong, S. Koljenović, and G. J. Puppels, “Discrimination between oral cancer and healthy tissue based on water content determined by Raman spectroscopy,” Anal. Chem. 87(4), 2419–2426 (2015).
    [Crossref]
  10. R. S. Brouwer de Koning, E. J. M. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. A. Smit, R. Dirven, B. H. W. Hendriks, H. Sterenborg, and T. J. M. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 23, 1–8 (2018).
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  11. J. Wang, Y. Xu, and S.A. Boppart, “Review of optical coherence tomography in oncology,” J. Biomed. Opt. 22, 1–23 (2017).
    [Crossref]
  12. A. J. Moy and J. W. Tunnell, JW: Chapter 17 - Diffuse Reflectance Spectroscopy and Imaging. In: Imaging in Dermatology. edn. MR Hamblin, P Avci, and GK. Gupta, eds. (Academic Press, 2016), pp. 203–215.
  13. S. F. Malin, T. L. Ruchti, T. B. Blank, S. N. Thennadil, and S. L. Monfre, “Noninvasive prediction of glucose by near-infrared diffuse reflectance spectroscopy,” Clin. Chem. 45(9), 1651–1658 (1999).
    [Crossref]
  14. A. I. Mundo, G. J. Greening, M. J. Fahr, L. N. Hale, E. A. Bullard EA, N. Rajaram, and T. J. Muldoon, “Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response,” J. Biomed. Opt. 25, 1–16 (2020).
    [Crossref]
  15. D. Fabila, J. M. de la Rosa, E. Stolik, S. Moreno, K. Suárez-Álvarez, G. López-Navarrete, C. Guzmán, J. Aguirre-García, C. Acevedo-García, and D. Kershenobich, “In vivo assessment of liver fibrosis using diffuse reflectance and fluorescence spectroscopy: a proof of concept,” Photodiagnosis and Photodynamic Therapy 9(4), 376–382 (2012).
    [Crossref]
  16. J. L. Jayanthi, G. U. Nisha, S. Manju, E. K. Philip, P. Jeemon, K. V. Baiju, V. Y. Beena, and N. Subhash, “Diffuse reflectance spectroscopy: diagnostic accuracy of a non-invasive screening technique for early detection of malignant changes in the oral cavity,” BMJ Open1(1), e000071 (2011).
    [Crossref]
  17. S. Brouwer de Koning, E. J. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. Smit, R. Dirven, B. H. Hendriks, H. J. C. Sterenborg, and T. J. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 12, 121611 (2018).
  18. K. Lin, W. Zheng, and Z. Huang, “Integrated autofluorescence endoscopic imaging and point-wise spectroscopy for real-time in vivo tissue measurements,” J. Biomed. Opt. 15(4), 040507 (2010).
    [Crossref]
  19. K. Sokolov, R. Drezek, K. Gossage, and R. Richards-Kortum, “Reflectance spectroscopy with polarized light: is it sensitive to cellular and nuclear morphology,” Opt. Express 5(13), 302–317 (1999).
    [Crossref]
  20. A. Myakov, L. Nieman, L. Wicky, U. Utzinger, R. Richards-Kortum, and K. Sokolov, “Fiber optic probe for polarized reflectance spectroscopy in vivo: design and performance,” J. Biomed. Opt. 7(3), 388–397 (2002).
    [Crossref]
  21. M. Jimenez, S. Lam, C. Poh, and K. Sokolov, “Depth sensitive oblique polarized reflectance spectroscopy of oral epithelial tissue,” Proc. SPIE 9155, 20 (2014).
    [Crossref]
  22. R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized light-scattering spectroscopy,” Nat. Med. 7(11), 1245–1248 (2001).
    [Crossref]
  23. J. Wang, W. Zheng, K. Lin, and Z. Huang, “Integrated Mueller-matrix near-infrared imaging and point-wise spectroscopy improves colonic cancer detection,” Biomed. Opt Express 7(4), 1116–1126 (2016).
    [Crossref]
  24. V. Dremin, Z. Marcinkevics, E. Zherebtsov, A. Popov, A. Grabovskis, H. Kronberga, K. Geldnere, A. Doronin, I. Meglinski, and A. Bykov A, “Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning,” IEEE Trans. Med. Imaging 40(4), 1207–1216 (2021).
    [Crossref]
  25. B. Yu B, H. Fu H, T. Bydlon, J. E. Bender, and N. Ramanujam, “Diffuse reflectance spectroscopy with a self-calibrating fiber optic probe,” Opt. Lett. 33(16), 1783–1785 (2008).
    [Crossref]
  26. B. Yu, H. L. Fu, and N. Ramanujam, “Instrument independent diffuse reflectance spectroscopy,” J. Biomed. Opt. 16(1), 011010 (2011).
    [Crossref]
  27. B. Yu, A. Shah, V. K. Nagarajan, and D. G. Ferris, “Diffuse reflectance spectroscopy of epithelial tissue with a smart fiber-optic probe,” Biomed. Opt. Express 5(3), 675–689 (2014).
    [Crossref]
  28. V. K. Nagarajan, J. M. Ward, and B. Yu, “Association of liver tissue optical properties and thermal damage,” Lasers Surg. Med. 52(8), 779–787 (2020).
    [Crossref]
  29. R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016).
    [Crossref]
  30. J. Wang, M. S. Bergholt, W. Zheng, and Z. Huang, “Development of a beveled fiber-optic confocal Raman probe for enhancing in vivo epithelial tissue Raman measurements at endoscopy,” Opt. Lett. 38(13), 2321–2323 (2013).
    [Crossref]
  31. V. Tuchin, “Polarized light interaction with tissues,” J. Biomed. Opt. 21(7), 071114 (2016).
    [Crossref]
  32. J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Simultaneous fingerprint and high-wavenumber fiber-optic Raman spectroscopy improves in vivo diagnosis of esophageal squamous cell carcinoma at endoscopy,” Sci. Rep. 5(1), 12957 (2015).
    [Crossref]
  33. Y. V. Zontov, O. Y. Rodionova, S. V. Kucheryavskiy, and A. L. Pomerantsev, “PLS-DA –a MATLAB GUI tool for hard and soft approaches to partial least squares discriminant analysis,” Chemometrics and Intelligent Laboratory Systems 203, 104064 (2020).
    [Crossref]
  34. J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Fiber-optic Raman spectroscopy for in vivo diagnosis of gastric dysplasia,” Faraday Discuss. 187, 377–392 (2016).
    [Crossref]
  35. J. Wang J, W. Zheng W, K. Lin K, and Z. Huang, “Characterizing biochemical and morphological variations of clinically relevant anatomical locations of oral tissue in vivo with hybrid Raman spectroscopy and optical coherence tomography technique,” J. Biophotonics. 11(3), e201700113 (2018).
    [Crossref]
  36. A. Popov, A. Bykov, and I. Meglinski, “Influence of probe pressure on diffuse reflectance spectra of human skin measured in vivo,” J. Biomed. Opt. 11, 110504 (2017).
    [Crossref]
  37. R. A. Schwarz, D. Arifler, S. K. Chang, I. Pavlova, I. A. Hussain, V. Mack, B. Knight, R. Richards-Kortum, and A. M. Gillenwater, “Ball lens coupled fiber-optic probe for depth-resolved spectroscopy of epithelial tissue,” Opt. Lett. 30(10), 1159–1161 (2005).
    [Crossref]

2021 (2)

H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA A Cancer J Clin. 71(3), 209–249 (2021).
[Crossref]

V. Dremin, Z. Marcinkevics, E. Zherebtsov, A. Popov, A. Grabovskis, H. Kronberga, K. Geldnere, A. Doronin, I. Meglinski, and A. Bykov A, “Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning,” IEEE Trans. Med. Imaging 40(4), 1207–1216 (2021).
[Crossref]

2020 (3)

V. K. Nagarajan, J. M. Ward, and B. Yu, “Association of liver tissue optical properties and thermal damage,” Lasers Surg. Med. 52(8), 779–787 (2020).
[Crossref]

Y. V. Zontov, O. Y. Rodionova, S. V. Kucheryavskiy, and A. L. Pomerantsev, “PLS-DA –a MATLAB GUI tool for hard and soft approaches to partial least squares discriminant analysis,” Chemometrics and Intelligent Laboratory Systems 203, 104064 (2020).
[Crossref]

A. I. Mundo, G. J. Greening, M. J. Fahr, L. N. Hale, E. A. Bullard EA, N. Rajaram, and T. J. Muldoon, “Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response,” J. Biomed. Opt. 25, 1–16 (2020).
[Crossref]

2019 (1)

H. Martin, V. L. James, W. Xu, Y. C. Amy, and F. Baowei, “Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks,” J. Biomed. Opt. 24, 1–9 (2019).
[Crossref]

2018 (4)

T. J. W. Klein Nulent, R. Noorlag, E. M. Van Cann, F. A. Pameijer, S. M. Willems, A. Yesuratnam, A. Rosenberg, R. de Bree, and R. J. J. van Es, “Intraoral ultrasonography to measure tumor thickness of oral cancer: A systematic review and meta-analysis,” Oral Oncol. 77, 29–36 (2018).
[Crossref]

R. S. Brouwer de Koning, E. J. M. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. A. Smit, R. Dirven, B. H. W. Hendriks, H. Sterenborg, and T. J. M. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 23, 1–8 (2018).
[Crossref]

S. Brouwer de Koning, E. J. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. Smit, R. Dirven, B. H. Hendriks, H. J. C. Sterenborg, and T. J. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 12, 121611 (2018).

J. Wang J, W. Zheng W, K. Lin K, and Z. Huang, “Characterizing biochemical and morphological variations of clinically relevant anatomical locations of oral tissue in vivo with hybrid Raman spectroscopy and optical coherence tomography technique,” J. Biophotonics. 11(3), e201700113 (2018).
[Crossref]

2017 (4)

A. Popov, A. Bykov, and I. Meglinski, “Influence of probe pressure on diffuse reflectance spectra of human skin measured in vivo,” J. Biomed. Opt. 11, 110504 (2017).
[Crossref]

J. Wang, Y. Xu, and S.A. Boppart, “Review of optical coherence tomography in oncology,” J. Biomed. Opt. 22, 1–23 (2017).
[Crossref]

P. Pałasz, Ł. Adamski, M. Górska-Chrząstek, A. Starzyńska, and M. Studniarek, “Contemporary diagnostic imaging of oral squamous cell carcinoma - a review of literature,” Pol J Radiol 82, 193–202 (2017).
[Crossref]

W. Qin, W. Qi, T. Jin, H. Guo, and L. Xi, “In vivo oral imaging with integrated portable photoacoustic microscopy and optical coherence tomography,” Appl. Phys. Lett. 111(26), 263704 (2017).
[Crossref]

2016 (4)

J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Fiber-optic Raman spectroscopy for in vivo diagnosis of gastric dysplasia,” Faraday Discuss. 187, 377–392 (2016).
[Crossref]

V. Tuchin, “Polarized light interaction with tissues,” J. Biomed. Opt. 21(7), 071114 (2016).
[Crossref]

R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016).
[Crossref]

J. Wang, W. Zheng, K. Lin, and Z. Huang, “Integrated Mueller-matrix near-infrared imaging and point-wise spectroscopy improves colonic cancer detection,” Biomed. Opt Express 7(4), 1116–1126 (2016).
[Crossref]

2015 (2)

J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Simultaneous fingerprint and high-wavenumber fiber-optic Raman spectroscopy improves in vivo diagnosis of esophageal squamous cell carcinoma at endoscopy,” Sci. Rep. 5(1), 12957 (2015).
[Crossref]

E. M. Barroso, R. W. H. Smits, T. C. Bakker Schut, I. ten Hove, J. A. Hardillo, E. B. Wolvius, R. J. Baatenburg de Jong, S. Koljenović, and G. J. Puppels, “Discrimination between oral cancer and healthy tissue based on water content determined by Raman spectroscopy,” Anal. Chem. 87(4), 2419–2426 (2015).
[Crossref]

2014 (2)

B. Yu, A. Shah, V. K. Nagarajan, and D. G. Ferris, “Diffuse reflectance spectroscopy of epithelial tissue with a smart fiber-optic probe,” Biomed. Opt. Express 5(3), 675–689 (2014).
[Crossref]

M. Jimenez, S. Lam, C. Poh, and K. Sokolov, “Depth sensitive oblique polarized reflectance spectroscopy of oral epithelial tissue,” Proc. SPIE 9155, 20 (2014).
[Crossref]

2013 (1)

2012 (2)

D. Fabila, J. M. de la Rosa, E. Stolik, S. Moreno, K. Suárez-Álvarez, G. López-Navarrete, C. Guzmán, J. Aguirre-García, C. Acevedo-García, and D. Kershenobich, “In vivo assessment of liver fibrosis using diffuse reflectance and fluorescence spectroscopy: a proof of concept,” Photodiagnosis and Photodynamic Therapy 9(4), 376–382 (2012).
[Crossref]

C. K. Lee, T. T. Chi, C. T. Wu, M. T. Tsai, C. P. Chiang, and C. C. Yang, “Diagnosis of oral precancer with optical coherence tomography,” Biomed. Opt. Express 3(7), 1632–1646 (2012).
[Crossref]

2011 (1)

B. Yu, H. L. Fu, and N. Ramanujam, “Instrument independent diffuse reflectance spectroscopy,” J. Biomed. Opt. 16(1), 011010 (2011).
[Crossref]

2010 (1)

K. Lin, W. Zheng, and Z. Huang, “Integrated autofluorescence endoscopic imaging and point-wise spectroscopy for real-time in vivo tissue measurements,” J. Biomed. Opt. 15(4), 040507 (2010).
[Crossref]

2009 (1)

S. Tiziani, V. Lopes, and U. L. Günther, “Early stage diagnosis of oral cancer using 1H NMR-based metabolomics,” Neoplasia 11(3), 269–IN10 (2009).
[Crossref]

2008 (1)

2005 (1)

2002 (1)

A. Myakov, L. Nieman, L. Wicky, U. Utzinger, R. Richards-Kortum, and K. Sokolov, “Fiber optic probe for polarized reflectance spectroscopy in vivo: design and performance,” J. Biomed. Opt. 7(3), 388–397 (2002).
[Crossref]

2001 (1)

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized light-scattering spectroscopy,” Nat. Med. 7(11), 1245–1248 (2001).
[Crossref]

1999 (3)

K. Sokolov, R. Drezek, K. Gossage, and R. Richards-Kortum, “Reflectance spectroscopy with polarized light: is it sensitive to cellular and nuclear morphology,” Opt. Express 5(13), 302–317 (1999).
[Crossref]

S. F. Malin, T. L. Ruchti, T. B. Blank, S. N. Thennadil, and S. L. Monfre, “Noninvasive prediction of glucose by near-infrared diffuse reflectance spectroscopy,” Clin. Chem. 45(9), 1651–1658 (1999).
[Crossref]

C. Y. Wang, H. K. Chiang, C. T. Chen, C. P. Chiang, Y. S. Kuo, and S. N. Chow, “Diagnosis of oral cancer by light-induced autofluorescence spectroscopy using double excitation wavelengths,” Oral Oncol. 35(2), 144–150 (1999).
[Crossref]

Acevedo-García, C.

D. Fabila, J. M. de la Rosa, E. Stolik, S. Moreno, K. Suárez-Álvarez, G. López-Navarrete, C. Guzmán, J. Aguirre-García, C. Acevedo-García, and D. Kershenobich, “In vivo assessment of liver fibrosis using diffuse reflectance and fluorescence spectroscopy: a proof of concept,” Photodiagnosis and Photodynamic Therapy 9(4), 376–382 (2012).
[Crossref]

Adamski, L.

P. Pałasz, Ł. Adamski, M. Górska-Chrząstek, A. Starzyńska, and M. Studniarek, “Contemporary diagnostic imaging of oral squamous cell carcinoma - a review of literature,” Pol J Radiol 82, 193–202 (2017).
[Crossref]

Aguirre-García, J.

D. Fabila, J. M. de la Rosa, E. Stolik, S. Moreno, K. Suárez-Álvarez, G. López-Navarrete, C. Guzmán, J. Aguirre-García, C. Acevedo-García, and D. Kershenobich, “In vivo assessment of liver fibrosis using diffuse reflectance and fluorescence spectroscopy: a proof of concept,” Photodiagnosis and Photodynamic Therapy 9(4), 376–382 (2012).
[Crossref]

Amy, Y. C.

H. Martin, V. L. James, W. Xu, Y. C. Amy, and F. Baowei, “Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks,” J. Biomed. Opt. 24, 1–9 (2019).
[Crossref]

Arifler, D.

Backman, V.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized light-scattering spectroscopy,” Nat. Med. 7(11), 1245–1248 (2001).
[Crossref]

Badizadegan, K.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized light-scattering spectroscopy,” Nat. Med. 7(11), 1245–1248 (2001).
[Crossref]

Baiju, K. V.

J. L. Jayanthi, G. U. Nisha, S. Manju, E. K. Philip, P. Jeemon, K. V. Baiju, V. Y. Beena, and N. Subhash, “Diffuse reflectance spectroscopy: diagnostic accuracy of a non-invasive screening technique for early detection of malignant changes in the oral cavity,” BMJ Open1(1), e000071 (2011).
[Crossref]

Bakker Schut, T. C.

R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016).
[Crossref]

E. M. Barroso, R. W. H. Smits, T. C. Bakker Schut, I. ten Hove, J. A. Hardillo, E. B. Wolvius, R. J. Baatenburg de Jong, S. Koljenović, and G. J. Puppels, “Discrimination between oral cancer and healthy tissue based on water content determined by Raman spectroscopy,” Anal. Chem. 87(4), 2419–2426 (2015).
[Crossref]

Baltussen, E. J.

S. Brouwer de Koning, E. J. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. Smit, R. Dirven, B. H. Hendriks, H. J. C. Sterenborg, and T. J. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 12, 121611 (2018).

Baltussen, E. J. M.

R. S. Brouwer de Koning, E. J. M. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. A. Smit, R. Dirven, B. H. W. Hendriks, H. Sterenborg, and T. J. M. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 23, 1–8 (2018).
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A. I. Mundo, G. J. Greening, M. J. Fahr, L. N. Hale, E. A. Bullard EA, N. Rajaram, and T. J. Muldoon, “Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response,” J. Biomed. Opt. 25, 1–16 (2020).
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V. Dremin, Z. Marcinkevics, E. Zherebtsov, A. Popov, A. Grabovskis, H. Kronberga, K. Geldnere, A. Doronin, I. Meglinski, and A. Bykov A, “Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning,” IEEE Trans. Med. Imaging 40(4), 1207–1216 (2021).
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R. S. Brouwer de Koning, E. J. M. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. A. Smit, R. Dirven, B. H. W. Hendriks, H. Sterenborg, and T. J. M. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 23, 1–8 (2018).
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S. Brouwer de Koning, E. J. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. Smit, R. Dirven, B. H. Hendriks, H. J. C. Sterenborg, and T. J. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 12, 121611 (2018).

R. S. Brouwer de Koning, E. J. M. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. A. Smit, R. Dirven, B. H. W. Hendriks, H. Sterenborg, and T. J. M. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 23, 1–8 (2018).
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A. I. Mundo, G. J. Greening, M. J. Fahr, L. N. Hale, E. A. Bullard EA, N. Rajaram, and T. J. Muldoon, “Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response,” J. Biomed. Opt. 25, 1–16 (2020).
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R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized light-scattering spectroscopy,” Nat. Med. 7(11), 1245–1248 (2001).
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A. I. Mundo, G. J. Greening, M. J. Fahr, L. N. Hale, E. A. Bullard EA, N. Rajaram, and T. J. Muldoon, “Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response,” J. Biomed. Opt. 25, 1–16 (2020).
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S. Tiziani, V. Lopes, and U. L. Günther, “Early stage diagnosis of oral cancer using 1H NMR-based metabolomics,” Neoplasia 11(3), 269–IN10 (2009).
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W. Qin, W. Qi, T. Jin, H. Guo, and L. Xi, “In vivo oral imaging with integrated portable photoacoustic microscopy and optical coherence tomography,” Appl. Phys. Lett. 111(26), 263704 (2017).
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R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized light-scattering spectroscopy,” Nat. Med. 7(11), 1245–1248 (2001).
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A. I. Mundo, G. J. Greening, M. J. Fahr, L. N. Hale, E. A. Bullard EA, N. Rajaram, and T. J. Muldoon, “Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response,” J. Biomed. Opt. 25, 1–16 (2020).
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S. Brouwer de Koning, E. J. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. Smit, R. Dirven, B. H. Hendriks, H. J. C. Sterenborg, and T. J. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 12, 121611 (2018).

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R. S. Brouwer de Koning, E. J. M. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. A. Smit, R. Dirven, B. H. W. Hendriks, H. Sterenborg, and T. J. M. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 23, 1–8 (2018).
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J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Fiber-optic Raman spectroscopy for in vivo diagnosis of gastric dysplasia,” Faraday Discuss. 187, 377–392 (2016).
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J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Fiber-optic Raman spectroscopy for in vivo diagnosis of gastric dysplasia,” Faraday Discuss. 187, 377–392 (2016).
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J. Wang, W. Zheng, K. Lin, and Z. Huang, “Integrated Mueller-matrix near-infrared imaging and point-wise spectroscopy improves colonic cancer detection,” Biomed. Opt Express 7(4), 1116–1126 (2016).
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J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Simultaneous fingerprint and high-wavenumber fiber-optic Raman spectroscopy improves in vivo diagnosis of esophageal squamous cell carcinoma at endoscopy,” Sci. Rep. 5(1), 12957 (2015).
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Itzkan, I.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized light-scattering spectroscopy,” Nat. Med. 7(11), 1245–1248 (2001).
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H. Martin, V. L. James, W. Xu, Y. C. Amy, and F. Baowei, “Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks,” J. Biomed. Opt. 24, 1–9 (2019).
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J. L. Jayanthi, G. U. Nisha, S. Manju, E. K. Philip, P. Jeemon, K. V. Baiju, V. Y. Beena, and N. Subhash, “Diffuse reflectance spectroscopy: diagnostic accuracy of a non-invasive screening technique for early detection of malignant changes in the oral cavity,” BMJ Open1(1), e000071 (2011).
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J. L. Jayanthi, G. U. Nisha, S. Manju, E. K. Philip, P. Jeemon, K. V. Baiju, V. Y. Beena, and N. Subhash, “Diffuse reflectance spectroscopy: diagnostic accuracy of a non-invasive screening technique for early detection of malignant changes in the oral cavity,” BMJ Open1(1), e000071 (2011).
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H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA A Cancer J Clin. 71(3), 209–249 (2021).
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M. Jimenez, S. Lam, C. Poh, and K. Sokolov, “Depth sensitive oblique polarized reflectance spectroscopy of oral epithelial tissue,” Proc. SPIE 9155, 20 (2014).
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W. Qin, W. Qi, T. Jin, H. Guo, and L. Xi, “In vivo oral imaging with integrated portable photoacoustic microscopy and optical coherence tomography,” Appl. Phys. Lett. 111(26), 263704 (2017).
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Karakullukcu, M. B.

R. S. Brouwer de Koning, E. J. M. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. A. Smit, R. Dirven, B. H. W. Hendriks, H. Sterenborg, and T. J. M. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 23, 1–8 (2018).
[Crossref]

S. Brouwer de Koning, E. J. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. Smit, R. Dirven, B. H. Hendriks, H. J. C. Sterenborg, and T. J. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 12, 121611 (2018).

Kershenobich, D.

D. Fabila, J. M. de la Rosa, E. Stolik, S. Moreno, K. Suárez-Álvarez, G. López-Navarrete, C. Guzmán, J. Aguirre-García, C. Acevedo-García, and D. Kershenobich, “In vivo assessment of liver fibrosis using diffuse reflectance and fluorescence spectroscopy: a proof of concept,” Photodiagnosis and Photodynamic Therapy 9(4), 376–382 (2012).
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Koljenovic, S.

R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016).
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E. M. Barroso, R. W. H. Smits, T. C. Bakker Schut, I. ten Hove, J. A. Hardillo, E. B. Wolvius, R. J. Baatenburg de Jong, S. Koljenović, and G. J. Puppels, “Discrimination between oral cancer and healthy tissue based on water content determined by Raman spectroscopy,” Anal. Chem. 87(4), 2419–2426 (2015).
[Crossref]

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V. Dremin, Z. Marcinkevics, E. Zherebtsov, A. Popov, A. Grabovskis, H. Kronberga, K. Geldnere, A. Doronin, I. Meglinski, and A. Bykov A, “Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning,” IEEE Trans. Med. Imaging 40(4), 1207–1216 (2021).
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C. Y. Wang, H. K. Chiang, C. T. Chen, C. P. Chiang, Y. S. Kuo, and S. N. Chow, “Diagnosis of oral cancer by light-induced autofluorescence spectroscopy using double excitation wavelengths,” Oral Oncol. 35(2), 144–150 (1999).
[Crossref]

Lam, S.

M. Jimenez, S. Lam, C. Poh, and K. Sokolov, “Depth sensitive oblique polarized reflectance spectroscopy of oral epithelial tissue,” Proc. SPIE 9155, 20 (2014).
[Crossref]

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R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016).
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H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA A Cancer J Clin. 71(3), 209–249 (2021).
[Crossref]

Lee, C. K.

Lin, K.

J. Wang, W. Zheng, K. Lin, and Z. Huang, “Integrated Mueller-matrix near-infrared imaging and point-wise spectroscopy improves colonic cancer detection,” Biomed. Opt Express 7(4), 1116–1126 (2016).
[Crossref]

J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Fiber-optic Raman spectroscopy for in vivo diagnosis of gastric dysplasia,” Faraday Discuss. 187, 377–392 (2016).
[Crossref]

J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Simultaneous fingerprint and high-wavenumber fiber-optic Raman spectroscopy improves in vivo diagnosis of esophageal squamous cell carcinoma at endoscopy,” Sci. Rep. 5(1), 12957 (2015).
[Crossref]

K. Lin, W. Zheng, and Z. Huang, “Integrated autofluorescence endoscopic imaging and point-wise spectroscopy for real-time in vivo tissue measurements,” J. Biomed. Opt. 15(4), 040507 (2010).
[Crossref]

Lin K, K.

J. Wang J, W. Zheng W, K. Lin K, and Z. Huang, “Characterizing biochemical and morphological variations of clinically relevant anatomical locations of oral tissue in vivo with hybrid Raman spectroscopy and optical coherence tomography technique,” J. Biophotonics. 11(3), e201700113 (2018).
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S. Tiziani, V. Lopes, and U. L. Günther, “Early stage diagnosis of oral cancer using 1H NMR-based metabolomics,” Neoplasia 11(3), 269–IN10 (2009).
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J. L. Jayanthi, G. U. Nisha, S. Manju, E. K. Philip, P. Jeemon, K. V. Baiju, V. Y. Beena, and N. Subhash, “Diffuse reflectance spectroscopy: diagnostic accuracy of a non-invasive screening technique for early detection of malignant changes in the oral cavity,” BMJ Open1(1), e000071 (2011).
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H. Martin, V. L. James, W. Xu, Y. C. Amy, and F. Baowei, “Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks,” J. Biomed. Opt. 24, 1–9 (2019).
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R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016).
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V. Dremin, Z. Marcinkevics, E. Zherebtsov, A. Popov, A. Grabovskis, H. Kronberga, K. Geldnere, A. Doronin, I. Meglinski, and A. Bykov A, “Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning,” IEEE Trans. Med. Imaging 40(4), 1207–1216 (2021).
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R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016).
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S. F. Malin, T. L. Ruchti, T. B. Blank, S. N. Thennadil, and S. L. Monfre, “Noninvasive prediction of glucose by near-infrared diffuse reflectance spectroscopy,” Clin. Chem. 45(9), 1651–1658 (1999).
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D. Fabila, J. M. de la Rosa, E. Stolik, S. Moreno, K. Suárez-Álvarez, G. López-Navarrete, C. Guzmán, J. Aguirre-García, C. Acevedo-García, and D. Kershenobich, “In vivo assessment of liver fibrosis using diffuse reflectance and fluorescence spectroscopy: a proof of concept,” Photodiagnosis and Photodynamic Therapy 9(4), 376–382 (2012).
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A. J. Moy and J. W. Tunnell, JW: Chapter 17 - Diffuse Reflectance Spectroscopy and Imaging. In: Imaging in Dermatology. edn. MR Hamblin, P Avci, and GK. Gupta, eds. (Academic Press, 2016), pp. 203–215.

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A. I. Mundo, G. J. Greening, M. J. Fahr, L. N. Hale, E. A. Bullard EA, N. Rajaram, and T. J. Muldoon, “Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response,” J. Biomed. Opt. 25, 1–16 (2020).
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V. K. Nagarajan, J. M. Ward, and B. Yu, “Association of liver tissue optical properties and thermal damage,” Lasers Surg. Med. 52(8), 779–787 (2020).
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A. Myakov, L. Nieman, L. Wicky, U. Utzinger, R. Richards-Kortum, and K. Sokolov, “Fiber optic probe for polarized reflectance spectroscopy in vivo: design and performance,” J. Biomed. Opt. 7(3), 388–397 (2002).
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J. L. Jayanthi, G. U. Nisha, S. Manju, E. K. Philip, P. Jeemon, K. V. Baiju, V. Y. Beena, and N. Subhash, “Diffuse reflectance spectroscopy: diagnostic accuracy of a non-invasive screening technique for early detection of malignant changes in the oral cavity,” BMJ Open1(1), e000071 (2011).
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P. Pałasz, Ł. Adamski, M. Górska-Chrząstek, A. Starzyńska, and M. Studniarek, “Contemporary diagnostic imaging of oral squamous cell carcinoma - a review of literature,” Pol J Radiol 82, 193–202 (2017).
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T. J. W. Klein Nulent, R. Noorlag, E. M. Van Cann, F. A. Pameijer, S. M. Willems, A. Yesuratnam, A. Rosenberg, R. de Bree, and R. J. J. van Es, “Intraoral ultrasonography to measure tumor thickness of oral cancer: A systematic review and meta-analysis,” Oral Oncol. 77, 29–36 (2018).
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J. L. Jayanthi, G. U. Nisha, S. Manju, E. K. Philip, P. Jeemon, K. V. Baiju, V. Y. Beena, and N. Subhash, “Diffuse reflectance spectroscopy: diagnostic accuracy of a non-invasive screening technique for early detection of malignant changes in the oral cavity,” BMJ Open1(1), e000071 (2011).
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V. Dremin, Z. Marcinkevics, E. Zherebtsov, A. Popov, A. Grabovskis, H. Kronberga, K. Geldnere, A. Doronin, I. Meglinski, and A. Bykov A, “Skin complications of diabetes mellitus revealed by polarized hyperspectral imaging and machine learning,” IEEE Trans. Med. Imaging 40(4), 1207–1216 (2021).
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A. Popov, A. Bykov, and I. Meglinski, “Influence of probe pressure on diffuse reflectance spectra of human skin measured in vivo,” J. Biomed. Opt. 11, 110504 (2017).
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E. M. Barroso, R. W. H. Smits, T. C. Bakker Schut, I. ten Hove, J. A. Hardillo, E. B. Wolvius, R. J. Baatenburg de Jong, S. Koljenović, and G. J. Puppels, “Discrimination between oral cancer and healthy tissue based on water content determined by Raman spectroscopy,” Anal. Chem. 87(4), 2419–2426 (2015).
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W. Qin, W. Qi, T. Jin, H. Guo, and L. Xi, “In vivo oral imaging with integrated portable photoacoustic microscopy and optical coherence tomography,” Appl. Phys. Lett. 111(26), 263704 (2017).
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W. Qin, W. Qi, T. Jin, H. Guo, and L. Xi, “In vivo oral imaging with integrated portable photoacoustic microscopy and optical coherence tomography,” Appl. Phys. Lett. 111(26), 263704 (2017).
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A. I. Mundo, G. J. Greening, M. J. Fahr, L. N. Hale, E. A. Bullard EA, N. Rajaram, and T. J. Muldoon, “Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response,” J. Biomed. Opt. 25, 1–16 (2020).
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Rodionova, O. Y.

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T. J. W. Klein Nulent, R. Noorlag, E. M. Van Cann, F. A. Pameijer, S. M. Willems, A. Yesuratnam, A. Rosenberg, R. de Bree, and R. J. J. van Es, “Intraoral ultrasonography to measure tumor thickness of oral cancer: A systematic review and meta-analysis,” Oral Oncol. 77, 29–36 (2018).
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S. F. Malin, T. L. Ruchti, T. B. Blank, S. N. Thennadil, and S. L. Monfre, “Noninvasive prediction of glucose by near-infrared diffuse reflectance spectroscopy,” Clin. Chem. 45(9), 1651–1658 (1999).
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Ruers, T. J. M.

R. S. Brouwer de Koning, E. J. M. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. A. Smit, R. Dirven, B. H. W. Hendriks, H. Sterenborg, and T. J. M. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 23, 1–8 (2018).
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Sewnaik, A.

R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016).
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Siegel, R. L.

H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA A Cancer J Clin. 71(3), 209–249 (2021).
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S. Brouwer de Koning, E. J. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. Smit, R. Dirven, B. H. Hendriks, H. J. C. Sterenborg, and T. J. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 12, 121611 (2018).

Smit, L. A.

R. S. Brouwer de Koning, E. J. M. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. A. Smit, R. Dirven, B. H. W. Hendriks, H. Sterenborg, and T. J. M. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 23, 1–8 (2018).
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R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016).
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E. M. Barroso, R. W. H. Smits, T. C. Bakker Schut, I. ten Hove, J. A. Hardillo, E. B. Wolvius, R. J. Baatenburg de Jong, S. Koljenović, and G. J. Puppels, “Discrimination between oral cancer and healthy tissue based on water content determined by Raman spectroscopy,” Anal. Chem. 87(4), 2419–2426 (2015).
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H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA A Cancer J Clin. 71(3), 209–249 (2021).
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M. Jimenez, S. Lam, C. Poh, and K. Sokolov, “Depth sensitive oblique polarized reflectance spectroscopy of oral epithelial tissue,” Proc. SPIE 9155, 20 (2014).
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A. Myakov, L. Nieman, L. Wicky, U. Utzinger, R. Richards-Kortum, and K. Sokolov, “Fiber optic probe for polarized reflectance spectroscopy in vivo: design and performance,” J. Biomed. Opt. 7(3), 388–397 (2002).
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P. Pałasz, Ł. Adamski, M. Górska-Chrząstek, A. Starzyńska, and M. Studniarek, “Contemporary diagnostic imaging of oral squamous cell carcinoma - a review of literature,” Pol J Radiol 82, 193–202 (2017).
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R. S. Brouwer de Koning, E. J. M. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. A. Smit, R. Dirven, B. H. W. Hendriks, H. Sterenborg, and T. J. M. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 23, 1–8 (2018).
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S. Brouwer de Koning, E. J. Baltussen, M. B. Karakullukcu, B. Dashtbozorg, L. Smit, R. Dirven, B. H. Hendriks, H. J. C. Sterenborg, and T. J. Ruers, “Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe,” J. Biomed. Opt. 12, 121611 (2018).

Stolik, E.

D. Fabila, J. M. de la Rosa, E. Stolik, S. Moreno, K. Suárez-Álvarez, G. López-Navarrete, C. Guzmán, J. Aguirre-García, C. Acevedo-García, and D. Kershenobich, “In vivo assessment of liver fibrosis using diffuse reflectance and fluorescence spectroscopy: a proof of concept,” Photodiagnosis and Photodynamic Therapy 9(4), 376–382 (2012).
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P. Pałasz, Ł. Adamski, M. Górska-Chrząstek, A. Starzyńska, and M. Studniarek, “Contemporary diagnostic imaging of oral squamous cell carcinoma - a review of literature,” Pol J Radiol 82, 193–202 (2017).
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D. Fabila, J. M. de la Rosa, E. Stolik, S. Moreno, K. Suárez-Álvarez, G. López-Navarrete, C. Guzmán, J. Aguirre-García, C. Acevedo-García, and D. Kershenobich, “In vivo assessment of liver fibrosis using diffuse reflectance and fluorescence spectroscopy: a proof of concept,” Photodiagnosis and Photodynamic Therapy 9(4), 376–382 (2012).
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J. L. Jayanthi, G. U. Nisha, S. Manju, E. K. Philip, P. Jeemon, K. V. Baiju, V. Y. Beena, and N. Subhash, “Diffuse reflectance spectroscopy: diagnostic accuracy of a non-invasive screening technique for early detection of malignant changes in the oral cavity,” BMJ Open1(1), e000071 (2011).
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H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA A Cancer J Clin. 71(3), 209–249 (2021).
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Teh, M.

J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Fiber-optic Raman spectroscopy for in vivo diagnosis of gastric dysplasia,” Faraday Discuss. 187, 377–392 (2016).
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J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Simultaneous fingerprint and high-wavenumber fiber-optic Raman spectroscopy improves in vivo diagnosis of esophageal squamous cell carcinoma at endoscopy,” Sci. Rep. 5(1), 12957 (2015).
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R. W. H. Smits, S. Koljenović, J. A. Hardillo, I. ten Hove, C. A. Meeuwis, A. Sewnaik, E. A. C. Dronkers, T. C. Bakker Schut, T. P. M. Langeveld, and J. Molenaar J, “Resection margins in oral cancer surgery: room for improvement,” Head Neck 38(S1), E2197–E2203 (2016).
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E. M. Barroso, R. W. H. Smits, T. C. Bakker Schut, I. ten Hove, J. A. Hardillo, E. B. Wolvius, R. J. Baatenburg de Jong, S. Koljenović, and G. J. Puppels, “Discrimination between oral cancer and healthy tissue based on water content determined by Raman spectroscopy,” Anal. Chem. 87(4), 2419–2426 (2015).
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S. F. Malin, T. L. Ruchti, T. B. Blank, S. N. Thennadil, and S. L. Monfre, “Noninvasive prediction of glucose by near-infrared diffuse reflectance spectroscopy,” Clin. Chem. 45(9), 1651–1658 (1999).
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A. J. Moy and J. W. Tunnell, JW: Chapter 17 - Diffuse Reflectance Spectroscopy and Imaging. In: Imaging in Dermatology. edn. MR Hamblin, P Avci, and GK. Gupta, eds. (Academic Press, 2016), pp. 203–215.

Utzinger, U.

A. Myakov, L. Nieman, L. Wicky, U. Utzinger, R. Richards-Kortum, and K. Sokolov, “Fiber optic probe for polarized reflectance spectroscopy in vivo: design and performance,” J. Biomed. Opt. 7(3), 388–397 (2002).
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T. J. W. Klein Nulent, R. Noorlag, E. M. Van Cann, F. A. Pameijer, S. M. Willems, A. Yesuratnam, A. Rosenberg, R. de Bree, and R. J. J. van Es, “Intraoral ultrasonography to measure tumor thickness of oral cancer: A systematic review and meta-analysis,” Oral Oncol. 77, 29–36 (2018).
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T. J. W. Klein Nulent, R. Noorlag, E. M. Van Cann, F. A. Pameijer, S. M. Willems, A. Yesuratnam, A. Rosenberg, R. de Bree, and R. J. J. van Es, “Intraoral ultrasonography to measure tumor thickness of oral cancer: A systematic review and meta-analysis,” Oral Oncol. 77, 29–36 (2018).
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C. Y. Wang, H. K. Chiang, C. T. Chen, C. P. Chiang, Y. S. Kuo, and S. N. Chow, “Diagnosis of oral cancer by light-induced autofluorescence spectroscopy using double excitation wavelengths,” Oral Oncol. 35(2), 144–150 (1999).
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J. Wang, Y. Xu, and S.A. Boppart, “Review of optical coherence tomography in oncology,” J. Biomed. Opt. 22, 1–23 (2017).
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J. Wang, W. Zheng, K. Lin, and Z. Huang, “Integrated Mueller-matrix near-infrared imaging and point-wise spectroscopy improves colonic cancer detection,” Biomed. Opt Express 7(4), 1116–1126 (2016).
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J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Fiber-optic Raman spectroscopy for in vivo diagnosis of gastric dysplasia,” Faraday Discuss. 187, 377–392 (2016).
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J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Simultaneous fingerprint and high-wavenumber fiber-optic Raman spectroscopy improves in vivo diagnosis of esophageal squamous cell carcinoma at endoscopy,” Sci. Rep. 5(1), 12957 (2015).
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V. K. Nagarajan, J. M. Ward, and B. Yu, “Association of liver tissue optical properties and thermal damage,” Lasers Surg. Med. 52(8), 779–787 (2020).
[Crossref]

Wicky, L.

A. Myakov, L. Nieman, L. Wicky, U. Utzinger, R. Richards-Kortum, and K. Sokolov, “Fiber optic probe for polarized reflectance spectroscopy in vivo: design and performance,” J. Biomed. Opt. 7(3), 388–397 (2002).
[Crossref]

Willems, S. M.

T. J. W. Klein Nulent, R. Noorlag, E. M. Van Cann, F. A. Pameijer, S. M. Willems, A. Yesuratnam, A. Rosenberg, R. de Bree, and R. J. J. van Es, “Intraoral ultrasonography to measure tumor thickness of oral cancer: A systematic review and meta-analysis,” Oral Oncol. 77, 29–36 (2018).
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E. M. Barroso, R. W. H. Smits, T. C. Bakker Schut, I. ten Hove, J. A. Hardillo, E. B. Wolvius, R. J. Baatenburg de Jong, S. Koljenović, and G. J. Puppels, “Discrimination between oral cancer and healthy tissue based on water content determined by Raman spectroscopy,” Anal. Chem. 87(4), 2419–2426 (2015).
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Wu, C. T.

Xi, L.

W. Qin, W. Qi, T. Jin, H. Guo, and L. Xi, “In vivo oral imaging with integrated portable photoacoustic microscopy and optical coherence tomography,” Appl. Phys. Lett. 111(26), 263704 (2017).
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Xu, W.

H. Martin, V. L. James, W. Xu, Y. C. Amy, and F. Baowei, “Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks,” J. Biomed. Opt. 24, 1–9 (2019).
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J. Wang, Y. Xu, and S.A. Boppart, “Review of optical coherence tomography in oncology,” J. Biomed. Opt. 22, 1–23 (2017).
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Yeoh, K. G.

J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Fiber-optic Raman spectroscopy for in vivo diagnosis of gastric dysplasia,” Faraday Discuss. 187, 377–392 (2016).
[Crossref]

J. Wang, K. Lin, W. Zheng, K. Y. Ho, M. Teh, K. G. Yeoh, and Z. Huang, “Simultaneous fingerprint and high-wavenumber fiber-optic Raman spectroscopy improves in vivo diagnosis of esophageal squamous cell carcinoma at endoscopy,” Sci. Rep. 5(1), 12957 (2015).
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T. J. W. Klein Nulent, R. Noorlag, E. M. Van Cann, F. A. Pameijer, S. M. Willems, A. Yesuratnam, A. Rosenberg, R. de Bree, and R. J. J. van Es, “Intraoral ultrasonography to measure tumor thickness of oral cancer: A systematic review and meta-analysis,” Oral Oncol. 77, 29–36 (2018).
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Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Figures (6)

Fig. 1.
Fig. 1. (a) Schematic of the rcPS-DR system developed for tissue measurements. (b) distal tip of the probe. Light emission diode (LED); spectrometer (Spec); excitation Fiber (Exc. Fiber); detection fiber (Det. Fiber); excitation fiber in the calibration channel (Calib. Exc. Fiber); detection fiber in the calibration channel (Calib. Det. Fiber); polytetrafluoroethylene for calibration (Calib. PTFE).
Fig. 2.
Fig. 2. (a) PS-DR system response function measured by both the DR standard and the rcPS-DR embedded diffuser. (b) rcPS-DR system response function changes under different fiber bending radiuses. (c) The rcPS-DR spectra under the same fiber bending conditions as in (b). (d) The calculated DLP (degree of linear polarization). Note: the results in (c, d) both uses flat reflection mirror (PF10-03-G01, Thorlabs, NJ, USA) as sample.
Fig. 3.
Fig. 3. (a, b) Raw PS-DR spectra reflected by the mirror under different levels of LED illumination of parallel (a, $ \| $), and perpendicular (b, ⊥) polarization detection channels, before calibration. (c, d) PS-DR spectra after calibration (i.e., ratio of the mirror-reflected intensity and the real-time calibrated PS-DR system response function). (e) DLP. The different mirror-reflected intensities in (a-b) are generated through adjustment of the LED driver current (10A, 6A, 3A, and 1.5A).
Fig. 4.
Fig. 4. (a) Schematic setup to determine the polarization detection capability of the rcPS-DR probe with external polarized light. (b, c) Raw PS-DR spectra for parallel (b, $ \| $), and perpendicular (c, ⊥) polarization detection channels, under different (0 to 360 degrees) rotation angles of the HWP as in (a). (d) Typical PS-DR spectra changes versus HWP rotation angles, and the resultant DLP. The wavelength was selected at 575 nm of (d, e). Multimode fiber (MMF); Collimator (Col); Linear polarizer (LP); Half waveplate (HWP).
Fig. 5.
Fig. 5. (a) Real-time calibrated PS-DR spectra (mean ± SE) measured from alveolar process, floor of mouth, and lateral tongues. (b). Box plot of the intensity ratios (mean ± SE) between I540 and I575 owing to oxygenated hemoglobin absorption. ○ Par and ◊ Perp correspond to the parallel- and perpendicular- polarization detection channels of the rcPS-DR probe. * (c) DLP (mean ± SE, standard error) of the alveolar process, floor of mouth, and lateral tongues. * P<0.05.
Fig. 6.
Fig. 6. (a) ANOVA of the three tissue categories over the entire PS-DR spectra wavelength. (b) posterior probabilities of 554 statistically different PS-DR spectra measured by the rcPS-DR handheld probe belonging to alveolar process (n = 226), floor of mouth (n = 178), and lateral tongue (n = 150).

Tables (2)

Tables Icon

Table 1. The detailed tissue types break down and sample distribution. AP: alveolar process. FM: floor of mouth; LT: lateral tongue.

Tables Icon

Table 2. Confusion matrix detailing the multiclass classification results of PS-DR spectra of different oral tissues using PLS-DA and LOSCV. AP: alveolar process. FM: floor of mouth; LT: lateral tongue.

Equations (1)

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D L P = I | | ( λ ) I ( λ ) I | | ( λ ) + I ( λ )

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