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

Optical coherence tomography: Promising imaging technique for the diagnosis of oral mucosal diseases

OBJECTIVE

This review aims to summarize the latest application of optical coherence tomography (OCT) in oral mucosal diseases, promoting an accurate and earlier diagnosis of such disorders, which are difficult to be differentiated.

SUBJECTIVE AND METHODS

References on the application of OCT in oral mucosal diseases were mainly obtained from PubMed, Embase, Web of Science and Scopus databases, using the keywords: "optical coherence tomography and 'oral mucosa/oral cancers/oral potentially malignant diseases/oral lichen planus/oral leukoplakia/oral erythroplakia/discoid lupus erythematosus/oral autoimmune bullous diseases/oral ulcers/erythema multiforme/oral mucositis'".

RESULTS

It is found that OCT is showing a promising application potential in the early detection, diagnosis, differential diagnosis, monitoring of oral cancer and oral dysplastic lesions, as well as the delineation of tumor margins. OCT is also playing an increasingly important role in the diagnosis of oral potentially malignant disorders, oral mucosal bullous diseases, oral ulcerative diseases, erythema multiforme, and the early detection of oral mucositis.

CONCLUSION

Optical coherence tomography, as a novel optical technique featured by real-time, noninvasive, dynamic and high-resolution imaging, is of great use to serve as an adjunct tool for the diagnosis, differential diagnosis, monitoring and therapy evaluation of oral mucosal diseases.

Discrimination of healthy oral tissue from oral cancer based on the mean grey value determined by optical coherence tomography

OBJECTIVE

This study aimed to identify a quantitative index for optical coherence tomography (OCT) images to discriminate tumours from surrounding tissues.

SUBJECTS AND METHODS

Based on OCT measurements, mean grey values were determined from 432 locations on fifty-four human tissue specimens (eighteen cancerous, para-cancerous, and normal tissues each). These results were histologically evaluated by hematoxylin and eosin staining (H&E).

RESULTS

The mean grey values of oral squamous cell carcinoma (OSCC) measurements were significantly different from those of the surrounding healthy tissue (p value < 0.0001), with the former being higher. The sensitivity and specificity of detecting tumourous tissue using this approach were 93 and 94%, respectively.

CONCLUSIONS

OCT as a non-invasive, real-time imaging method, correlates well with H&E pathological images. It can effectively distinguish squamous cell carcinoma from normal tissues with high sensitivity and specificity and is thus expected to assist and guide tumour margin evaluation.

CLINICAL RELEVANCE

This discovery highlights the potential of OCT in the objective evaluation of tumour margin during surgery.

Imaging Biomarkers of Oral Dysplasia and Carcinoma Measured with In Vivo Endoscopic Optical Coherence Tomography

Optical coherence tomography is a noninvasive imaging technique that provides three-dimensional visualization of subsurface tissue structures. OCT has been proposed and explored in the literature as a tool to assess oral cancer status, select biopsy sites, or identify surgical margins. Our endoscopic OCT device can generate widefield (centimeters long) imaging of lesions at any location in the oral cavity-but it is challenging for raters to quantitatively assess and score large volumes of data. Leveraging a previously developed epithelial segmentation network, this work develops quantifiable biomarkers that provide direct measurements of tissue properties in three dimensions. We hypothesize that features related to morphology, tissue attenuation, and contrast between tissue layers will be able to provide a quantitative assessment of disease status (dysplasia through carcinoma). This work retrospectively assesses seven biomarkers on a lesion-contralateral matched OCT dataset of the lateral and ventral tongue (40 patients, 70 sites). Epithelial depth and loss of epithelial-stromal boundary visualization provide the strongest discrimination between disease states. The stroma optical attenuation coefficient provides a distinction between benign lesions from dysplasia and carcinoma. The stratification biomarkers visualize subsurface changes, which provides potential for future utility in biopsy site selection or treatment margin delineation.

Diagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: a systematic review and meta-analysis

BACKGROUND

The objective of this study is to examine the application of artificial intelligence (AI) algorithms in detecting oral potentially malignant disorders (OPMD) and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes.

MATERIALS AND METHODS

A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools.

RESULTS

The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68.438 (95% CI= [39.484-118.623], I2 =86%). The area under the SROC curve was 0.938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89.9% (95% CI= [0.866-0.925]; I2 =81%), specificity of 89.2% (95% CI= [0.851-0.922], I2 =79%), and a high negative predictive value of 89.5% (95% CI= [0.851-0.927], I2 =96%). Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49.30, and the area under the SROC curve was 0.877. Additionally, sensitivity, specificity, and negative predictive value were 90.5% (95% CI [0.873-0.929], I2 =4%), 87.0% (95% CI [0.813-0.912], I2 =49%) and 90.1% (95% CI [0.860-0.931], I2 =57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy.

CONCLUSIONS

AI-based detection using clinical photography shows a high DOR and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment.

Potential use of optical coherence tomography in oral potentially malignant disorders: in-vivo case series study

BACKGROUND

Evidence confirms that the use of Optical Coherence Tomography (OCT) in oral medicine can be a reliable aid for the diagnosis and management of Oral Potentially Malignant Disorders (OPMDs). Several authors described the ability of this system to detect the structural changes of the epithelia involved by the OPMDs. The purpose of this case series is to provide a suggestion for interpretation of OCT images from different OPMDs, compared to OCT images of healthy tissues.

METHODS

A sample of 11 OPMDs patients was recruited and analyzed with OCT. The images obtained were then compared with an OCT repertoire image. In this work the reflectance degree was considered, together with the analysis of the increased/decreased thicknesses of the various layers. Keratin Layer (KL), Epithelial Layer (EP), Lamina Propria (LP), Basal Membrane (BM) assessment, for each lesion, was performed.

RESULTS

OCT measurements of KL, EP and LP layers, together with BM assessing, should aid the physicians to recognize and describe different oral lesions, relating them to the corresponding oral pathology.

CONCLUSION

More studies like this, on larger samples, are needed to validate the results and provide, in the future, a kind of manual that could guide clinicians to correctly interpret the OCT images in relation to the causing pathologies.

TRIAL REGISTRATION

The present trial has been registered with ISRCTN (#17,893,224).

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

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.

Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

Simple Summary

Early detection of oral cancer is important to increase the survival rate and reduce morbidity. For the past few years, the early detection of oral cancer using artificial intelligence (AI) technology based on autofluorescence imaging, photographic imaging, and optical coherence tomography imaging has been an important research area. In this study, diagnostic values including sensitivity and specificity data were comprehensively confirmed in various studies that performed AI analysis of images. The diagnostic sensitivity of AI-assisted screening was 0.92. In subgroup analysis, there was no statistically significant difference in the diagnostic rate according to each image tool. AI shows good diagnostic performance with high sensitivity for oral cancer. Image analysis using AI is expected to be used as a clinical tool for early detection and evaluation of treatment efficacy for oral cancer.

Abstract

The accuracy of artificial intelligence (AI)-assisted discrimination of oral cancerous lesions from normal mucosa based on mucosal images was evaluated. Two authors independently reviewed the database until June 2022. Oral mucosal disorder, as recorded by photographic images, autofluorescence, and optical coherence tomography (OCT), was compared with the reference results by histology findings. True-positive, true-negative, false-positive, and false-negative data were extracted. Seven studies were included for discriminating oral cancerous lesions from normal mucosa. The diagnostic odds ratio (DOR) of AI-assisted screening was 121.66 (95% confidence interval [CI], 29.60; 500.05). Twelve studies were included for discriminating all oral precancerous lesions from normal mucosa. The DOR of screening was 63.02 (95% CI, 40.32; 98.49). Subgroup analysis showed that OCT was more diagnostically accurate (324.33 vs. 66.81 and 27.63) and more negatively predictive (0.94 vs. 0.93 and 0.84) than photographic images and autofluorescence on the screening for all oral precancerous lesions from normal mucosa. Automated detection of oral cancerous lesions by AI would be a rapid, non-invasive diagnostic tool that could provide immediate results on the diagnostic work-up of oral cancer. This method has the potential to be used as a clinical tool for the early diagnosis of pathological lesions.

Molecular Research on Oral Diseases and Related Biomaterials: A Journey from Oral Cell Models to Advanced Regenerative Perspectives

Oral diseases such as gingivitis, periodontitis, and oral cancer affect millions of people worldwide. Much research has been conducted to understand the pathogenetic mechanisms of these diseases and translate this knowledge into therapeutics. This review aims to take the reader on a journey from the initial molecular discoveries to complex regenerative issues in oral medicine. For this, a semi-systematic literature search was carried out in Medline and Web of Science databases to retrieve the primary literature describing oral cell models and biomaterial applications in oral regenerative medicine. First, an in vitro cell model of gingival keratinocytes is discussed, which illustrates patho- and physiologic principles in the context of oral epithelial homeostasis and carcinogenesis and represents a cellular tool to understand biomaterial-based approaches for periodontal tissue regeneration. Consequently, a layered gradient nonwoven (LGN) is described, which demonstrates that the key features of biomaterials serve as candidates for oral tissue regeneration. LGN supports proper tissue formation and obeys the important principles for molecular mechanotransduction. Furthermore, current biomaterial-based tissue regeneration trends, including polymer modifications, cell-based treatments, antimicrobial peptides and optogenetics, are introduced to represent the full spectrum of current approaches to oral disease mitigation and prevention. Altogether, this review is a foray through established and new concepts in oral regenerative medicine and illustrates the process of knowledge translation from basic molecular and cell biological research to future clinical applications.