Optical Coherence Tomography as an Oral Cancer Screening Adjunct in a Low Resource Settings

Heidari, Andrew E.; Sunny, Sumsum; James, Bonney Lee; Lam, Tracie; Tran, Anne V.; Yu, Jin‐Yi; Ramanjinappa, Ravindra D.; Uma, K.; Birur, Praveen; Suresh, Amritha; Kuriakose, Moni Abraham; Chen, Zhongping; Wilder‐Smith, Petra

doi:10.1109/jstqe.2018.2869643

Cited by 39 publications

(46 citation statements)

References 25 publications

(27 reference statements)

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“…[4,14,18,20,33,34] There are unique methods of analyzing the above scattering profiles pertaining to each condition, including SD and exponential decay constant of the depth resolved intensity signals, segmentation of visible layers, and average deviation of the basement membrane. [15,20] However, these image processing techniques have been used to classify lesions using individual 2D OCT images lacking a spatial understanding of the changing tissue anatomy in 3D. [14,19,20] No automated approach has been adapted to handle the large HNSCC 3D OCT data sets.…”

Section: Discussionmentioning

confidence: 99%

“…Tissue contrast does not: (1) depend upon biochemical absorbers such as in fluorescence imaging, (2) require the use of exogenous dyes or stains (requiring regulatory approval), and (3) does not require special modification of operating room ambient lighting such as in many fluorescent techniques. OCT has been shown to differentiate normal and abnormal oral mucosa . However, direct subjective interpretation of OCT images by human observers requires extensive training .…”

Section: Introductionmentioning

confidence: 99%

“…OCT has been shown to differentiate normal and abnormal oral mucosa. [4,[8][9][10][11][12][13][14][15][16] However, direct subjective interpretation of OCT images by human observers requires extensive training. [11] Since contemporary OCT systems may acquire more than 40 images/second, the overwhelming amount of data generated poses a challenge for clinical interpretation.…”

Section: Introductionmentioning

confidence: 99%

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The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa

Heidari

Pham

Ifegwu

et al. 2020

Journal of Biophotonics

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Incomplete surgical resection of head and neck squamous cell carcinoma (HNSCC) is the most common cause of local HNSCC recurrence. Currently, surgeons rely on preoperative imaging, direct visualization, palpation and frozen section to determine the extent of tissue resection. It has been demonstrated that optical coherence tomography (OCT), a minimally invasive, nonionizing near infrared mesoscopic imaging modality can resolve subsurface differences between normal and abnormal head and neck mucosa. Previous work has utilized two‐dimensional OCT imaging which is limited to the evaluation of small regions of interest generated frame by frame. OCT technology is capable of performing rapid volumetric imaging, but the capacity and expertise to analyze this massive amount of image data is lacking. In this study, we evaluate the ability of a retrained convolutional neural network to classify three‐dimensional OCT images of head and neck mucosa to differentiate normal and abnormal tissues with sensitivity and specificity of 100% and 70%, respectively. This method has the potential to serve as a real‐time analytic tool in the assessment of surgical margins.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa

Heidari

Pham

Ifegwu

et al. 2020

Journal of Biophotonics

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“… Optical coherence tomography White light Michelson interferometry Oral mucosa NA 90 % High-resolution images Fast acquisition time Non-invasive Easy-to-use Limited depth of penetration (1.5-2 mm) Light interference reduces sensitivity Negative influence on image quality due to hyperkeratosis. 1000-2500 [ 44 , 45 ] 24. Physical examination Systematic visual examination and palpation Oral cavity (in situ) NA NA Reduces morbidity, mortality, incidence of invasive cancers Identifies high-risk groups Low cost Detection in a late phase Requires skilled professional Chances of false positive and negative 200-500 [ 46 ] 25.…”

Section: Techniques Used For Diagnosis Of Oral Cancermentioning

confidence: 99%

“…In a recent study by Ghosh et al, Raman and FTIR data of exfoliated cells were used to distinguish the stages of oral cancer [ 107 ]. Few other spectroscopic methods used to detect the oral malignancy are elastic scattering spectroscopy [ 25 ], diffuse reflectance spectroscopy [ 22 ], and optical coherence tomography [ 44 ]. Light-based-detection systems (LBDS) have gained importance in the field of oral cancer diagnostics owing to its simplicity and ease of use.…”

Section: Techniques Used For Diagnosis Of Oral Cancermentioning

confidence: 99%

Oral cancer diagnosis and perspectives in India

Borse

Konwar

Buragohain

2020

Sensors International

258

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Globally, oral cancer is the sixth most common type of cancer with India contributing to almost one-third of the total burden and the second country having the highest number of oral cancer cases. Oral squamous cell carcinoma (OSCC) dominates all the oral cancer cases with potentially malignant disorders, which is also recognized as a detectable pre-clinical phase of oral cancer. Tobacco consumption including smokeless tobacco, betel-quid chewing, excessive alcohol consumption, unhygienic oral condition, and sustained viral infections that include the human papillomavirus are some of the risk aspects for the incidence of oral cancer. Lack of knowledge, variations in exposure to the environment, and behavioral risk factors indicate a wide variation in the global incidence and increases the mortality rate. This review describes various risk factors related to the occurrence of oral cancer, the statistics of the distribution of oral cancer in India by various virtues, and the socio-economic positions. The various conventional diagnostic techniques used routinely for detection of the oral cancer are discussed along with advanced techniques. This review also focusses on the novel techniques developed by Indian researchers that have huge potential for application in oral cancer diagnosis.

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Efficacy of optical coherence tomography in the diagnosing of oral cancerous lesion: systematic review and meta‐analysis

Kim

Hwang

2022

Head & Neck

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Non‐invasive diagnostic tools that facilitate visualization of potentially malignant oral lesions and cancers have been introduced. Oral lesions detected by optical coherence tomography (OCT) were compared to reference results based on histological findings. The diagnostic odds ratio (DOR), along with summary receiver operating characteristic curve (SROC), area under SROC, sensitivity, specificity, and negative predictive values, were the outcomes. The DOR of OCT was 86.9190 (95% confidence interval [CI]: 38.7435, 194.9985), and the area under SROC was 0.951. OCT showed good sensitivity (0.9138; 95% CI: 0.8758, 0.9409) and specificity (0.9110; 95% CI: 0.8568, 0.9460), and a high negative predictive value (0.9225; 95% CI: 0.8863, 0.9478). Diagnostic sensitivity was higher when using artificial intelligence and automated algorithms compared to diagnoses made by clinicians. OCT is non‐invasive, provides rapid results without radiation exposure, and can aid in the diagnosis and follow‐up of oral cancer and oral precancerous lesions.

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Optical Coherence Tomography as an Oral Cancer Screening Adjunct in a Low Resource Settings

Cited by 39 publications

References 25 publications

The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa

The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa

Oral cancer diagnosis and perspectives in India

Efficacy of optical coherence tomography in the diagnosing of oral cancerous lesion: systematic review and meta‐analysis

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