Intraoperative imaging of oral-maxillofacial lesions using optical coherence tomography

“…The layout of the portable SS‐OCT system and its specifications used in the hospital for intraoperative scanning has been reported in our previous articles [21,37,38]. The swept‐source with the output spectral tuning range of 87 nm, centered at 1310 nm, is used, which can provide an output power of 20 mW and a sweep rate of 100 kHz.…”

Section: Methodsmentioning

confidence: 99%

“…In oral medicine, OCT has been used to scan ex vivo and in vivo oral tissues [21‐23]. Hamster cheek pouches with induced dysplasia and malignancies were imaged by using OCT and the feasibility of OCT was evaluated for diagnosis of oral mucosal diseases [24‐26].…”

Section: Introductionmentioning

confidence: 99%

Classification of Salivary Gland Tumors Based on Quantitative Optical Coherence Tomography

et al. 2021

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Background and Objectives Visual inspection is the primary diagnostic method for oral diseases, and its accuracy of diagnosis mainly depends on surgeons’ experience. Histological examination is still the golden standard, but it is invasive and time‐consuming. In order to address these issues, as a noninvasive imaging technique, optical coherence tomography (OCT) can differentiate oral tissue with advantages of real‐time, in situ, and high resolution. The aim of this study is to explore optimal quantitative parameters in OCT images to distinguish different salivary gland tumors. Study Design/Materials and Methods OCT images of four salivary gland tumors were obtained from 14 patients, including mucoepidermoid carcinoma (MC), adenoid cystic carcinoma (ACC), basal cell adenoma (BCA), and pleomorphic adenoma (PA). Two parameters of optical attenuation coefficient (OAC) and standard deviation (SD) along the depth of OCT signal were combined to create a computational model of classification, and sensitivity/specificity of classification was calculated statistically to evaluate their results. Results A total of 5,919 two‐dimensional (2D) OCT images were used for quantitative analysis. The classification sensitivities of 89.6%, 95.0%, 89.5%, 97.8%, and specificities of 97.6%, 99.0%, 98.0%, 98.2%, respectively, were obtained for MC, ACC, BCA, and PA, with the thresholds of 3.6 mm−1 based on OAC and 0.22/0.18 based on SD. Conclusion It was demonstrated that OAC and SD could be considered as important parameters in quantitative analysis of OCT images for salivary gland tissue characterization and intraoperative diagnosis. It is of great potential value in promoting the application of this method based on OCT in clinical practice. Lasers Surg. © 2020 Wiley Periodicals LLC

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“…24 The previous work of our group showed that OCT images of oral-maxillofacial lesions had obvious distinctions based on different microstructural features. 25 On this basis, we investigated the quantitative difference of OSCC and oral non-cancer tissue based on an available optical attenuation model, and the diagnostic sensitivity (97.88%) and specificity (83.77%) were achieved at the attenuation threshold of 4.7 mm −1 . 26 However, the ability of OCT to distinguish between precancerous lesions and cancer is still unclear, which is more urgent and challenging.…”

Section: Introductionmentioning

confidence: 99%

“…Adegun et al quantitatively differentiated dysplasia from normal samples by measuring the rate of change of backscattered light intensity with depth in OCT and histological images 24 . The previous work of our group showed that OCT images of oral‐maxillofacial lesions had obvious distinctions based on different microstructural features 25 . On this basis, we investigated the quantitative difference of OSCC and oral non‐cancer tissue based on an available optical attenuation model, and the diagnostic sensitivity (97.88%) and specificity (83.77%) were achieved at the attenuation threshold of 4.7 mm −1 26 .…”

Section: Introductionmentioning

confidence: 99%

Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography

et al. 2021

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Background and Objectives: Distinguishing cancer from precancerous lesions is critical and challenging in oral medicine. As a noninvasive method, optical coherence tomography (OCT) has the advantages of real-time, in vivo, and large-depth imaging. Texture information hidden in OCT images can provide an important auxiliary effect for improving diagnostic accuracy. The aim of this study is to explore a reliable and accurate OCT-based method for the screening and diagnosis of human oral diseases, especially oral cancer. Materials and Methods: Fresh ex vivo oral tissues including normal mucosa, leukoplakia with epithelial hyperplasia (LEH), and oral squamous cell carcinoma (OSCC) were imaged intraoperatively by a homemade OCT system, and 58 texture features were extracted to create computational models of these tissues. A principal component analysis algorithm was employed to optimize the combination of texture feature vectors. The identification based on artificial neural network (ANN) was proposed and the sensitivity/specificity was calculated statistically to evaluate the classification performance. Results: A total of 71 sites of three types of oral tissues were measured, and 5176 OCT images of three types of oral tissues were used in this study. The superior classification result based on ANN was obtained with an average accuracy of 98.17%. The sensitivity and specificity of normal mucosa, LEH, and OSCC are 98.17% / 98.38%, 93.81% / 98.54%, and 98.11% / 99.04%, respectively. Conclusion: It is demonstrated from the high accuracies, sensitivities, and specificities that texture-based analysis can be used to identify oral precancerous and cancerous tissue in OCT images, and it has the potential to help surgeons in diseases screening and diagnosis effectively.

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Intraoperative imaging of oral-maxillofacial lesions using optical coherence tomography

Cited by 20 publications

References 26 publications

Axial Super-Resolution Study for Optical Coherence Tomography Images Via Deep Learning

Axial Super-Resolution Study for Optical Coherence Tomography Images Via Deep Learning

Classification of Salivary Gland Tumors Based on Quantitative Optical Coherence Tomography

Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography

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