Real-time calibrating polarization-sensitive diffuse reflectance handheld probe characterizes clinically relevant anatomical locations of oral tissue in vivo

Sub-diffuse Reflectance Spectroscopy Combined With Machine Learning Method for Oral Mucosal Disease Identification

OBJECTIVES

Oral squamous cell carcinoma (OSCC) is the sixth-highest incidence of malignant tumors worldwide. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Here, we present a sub-diffuse reflectance spectroscopy combined with a machine learning method for oral mucosal disease identification. This method provides a noninvasive cost-effective identification option for early signs of malignancy.

METHODS

Sub-diffuse spectra of three oral sites (hypoglottis, buccal, and gingiva) from healthy subjects and three types of mucosal lesions (oral lichen planus, OLP, oral leukoplakia, OLK, and OSCC) from patients were collected by using a home-made sub-diffuse reflectance spectroscopy prototype system, and three features including spectra ratio (SR), first-order derivative(DE) of the spectra and optical parameters (OP) were derived from the original spectra to enhance the insights into the optical properties of the oral mucosal tissues. To accurately classify the spectral features, a support vector machine (SVM) and probabilistic neural network (PNN) were employed.

RESULT

Most of the statistical distributions of the spectral features demonstrated obvious differences and the two classification methods exhibited comparable performances. For the classification in the oral sites of healthy subjects, the OP-based classification results were unsatisfactory, while the classification results utilizing DR, SR, and DE achieved a least accuracy of 0.8289, sensitivity of 0.8495, sensitivity of 0.9311, and Matthews correlation coefficient of 0.8085. Comparatively, the classification results between OLP, OLK, OSCC, and normal tissue obtained achieved high indexes even using the OP feature.

CONCLUSION

Integrating sub-diffuse reflectance spectroscopy measurement and suitable machine learning methods can obtain remarkable precision in differentiating different sites of oral mucosa and identifying different types of oral mucosal diseases, especially based on DE features. It is of great help in detecting OSCC and is expected to be a highly sensitive, time-sensitive, and accurate method for oral disease detection.

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.

Bimodal polarization-sensitive imaging and diffuse reflectance spectroscopy facilitated rapid augmented reality for tissue stratification

We reported the development of a bimodal large field of view (~95 cm² ) polarization-sensitive (PS) imaging and point-wise diffuse reflectance (DR) spectroscopy platform synergising the advantages of the two modalities. PS-DR spectroscopy allows simultaneous label-free biochemical and structural analysis of the investigated tissue. For real applications, the PS imaging first grossly stratified tissue into different categories, followed by high information dimension PS-DR spectroscopy ascertaining ambiguous tissue regions identified by the PS imaging. Tested on a tissue phantom consisting of porcine esophagus muscularis propria and submucosa, it has demonstrated the system developed could complete tissue interrogation within 0.6 minutes and with separation accuracy of 95.87%. Further test on tissue phantom consisting of natural and crushed bovine tendon mimicking normal and cancerous breast tissues validated the potential of system developed to effectively and rapidly investigate large tissue area, facilitating augmented tissue boundary demarcation.