Non-invasive imaging of oral potentially malignant and malignant lesions: A systematic review and meta-analysis

Adjunctive aids for the detection of oral squamous cell carcinoma and oral potentially malignant disorders: A systematic review of systematic reviews

This study presents the results of systematic reviews on adjunctive tools in screening and diagnosis of oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMD) and to determine if the current literature supports their use as either an adjunctive tool or replacement of gold standard techniques. Systemic reviews and meta-analysis that evaluated adjunctive tools including chemiluminescence, tissue autofluorescence, tissue fluorescence spectroscopy, vital staining and cytology techniques were systematically examined using AMSTAR II. Twenty-seven systematic reviews were included. Five studies had a low quality of evidence, and nine studies had a critically low quality of evidence. This review found limited evidence to recommend chemiluminescence, tissue autofluorescence tools and vital staining as diagnostic tools, but only serve as clinical adjuncts to conventional oral examination. Cytology techniques and narrow-band imaging may be utilised as a non-invasive diagnostic adjunctive tool for the detection of OSCC and the malignant transformation of OPMD. In conclusion, this paper provides evidence on several types of adjunctive tools and provides recommendations on their use in clinical practice. These tools are considered useful as clinical adjuncts but there is insufficient evidence for their use as a diagnostic tool to replace gold standard conventional oral examination and surgical biopsy.

Fluorescence Visualization-Guided Surgery Improves Local Control for Mandibular Squamous Cell Carcinoma

BACKGROUND

Local recurrence is common in mandibular squamous cell carcinoma (SCC). Fluorescence visualization is a noninvasive technology that can detect oral epithelial dysplasia around mandibular SCC, and it can potentially reduce local recurrence.

PURPOSE

The purpose of this study was to measure and compare local control (LC) between fluorescence visualization-guided surgery (FVS) and conventional surgery for patients with Stages I or II mandibular SCC.

STUDY DESIGN, SETTING, SAMPLE

This retrospective cohort study was conducted at Tokyo Dental College, Chiba Hospital, or Chiba Dental Center. The medical records of mandibular SCC patients from 2000 to 2021 were analyzed. Patients from any sex and 18 years of age or older with complete records who received surgery for mandibular SCC in the early stages were included in this study.

PREDICTOR VARIABLE

The predictor variable was operative treatment and was divided into 2 groups, conventional or FVS.

MAIN OUTCOME VARIABLES

The outcome variable is 5-year LC defined as no recurrence at or within 20 mm of the surgical site.

COVARIATES

Covariates included demographic variables of age, sex, clinical and pathological characteristics, forms of resection, lifestyle, and quality of life.

ANALYSES

Data analysis was performed by carrying out χ2 tests. Survival outcome was performed by the Kaplan-Meier method, which was used to calculate and stratify the log-rank test; P values <.05 indicated statistical significance.

RESULTS

This study sample was composed of 56 subjects with a mean age of 68.5 years old (standard deviation 13.7), and 33 (58.9%) were female. There were 36 (64.3%) and 20 (35.7%) subjects in the conventional and FVS groups. The characteristics and quality of life did not differ significantly between the 2 groups. Five-year LC with FVS was statistically significantly higher than conventional surgery (P = .04, 94.4 vs 77.2%). Multivariate analysis for LC rate only identified FVS (P = .004; hazard ratio = 0.11, 95% confidence interval = 0.46, 0.88).

CONCLUSION AND RELEVANCE

On mandibular SCC, LC was 94.4% in FVS versus 77.2% in conventional surgery. For mandibular SCC at stages I and II, FVS was associated with improved LC.

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.

Clinical aspects of oral cancer and potentially malignant disorders in South and Southeast Asia

BACKGROUND

Oral cancer and Oral Potentially Malignant Disorders (OPMD) are major health problems in South and Southeast Asia.

AIMS

To describe and discuss the clinical aspects of Oral Cancer and OPMD in South and Southeast Asia.

MATERIALS AND METHODS

Literature review of concepts and data over the last four decades.

DISCUSSION

Asian countries account for about two-thirds of new cases of oral cancer (OC) globally, with the highest burden in the South and Southeast Asian countries, including Pakistan and India. Habits, dietary patterns, socioeconomic status, and access to routine dental care play a crucial role in defining the demographics and clinical presentation of OC in these regions and significantly influence the morbidity and mortality of the disease. This region sees the use of different types of tobacco with or without areca nut (AN), such as pan masala, gutka, gul, snuff, mawa, and mishri. Tobacco use is high among men in Sri Lanka, Myanmar, Maldives, Bangladesh, Nepal, India and Bhutan. Areca nut is the fourth most common addictive substance globally and is frequently used in South and Southeast Asian countries, including Southeast China, Hainan Island, India, Taiwan, and the Pacific Islands, and immigrants from these regions in Africa, Europe, and North America. The use of these products results in mucosal alterations with varied clinical presentation of Oral Potentially Malignant Disorders (OPMDs) and OC. We discuss here the different types of OPMD and OC, the diagnostic aids and their relevance in clinical practice, and factors that influence their prognosis.

Quantification of oxygen consumption in head and neck cancer using fluorescent sensor foil technology

Introduction

Head and neck squamous cell carcinoma (HNSCC) patients suffer from frequent local recurrences that negatively impact on prognosis. Hence, distinguishing tumor and normal tissue is of clinical importance as it may improve the detection of residual tumor tissue in surgical resection margins and during imaging-based surgery planning. Differences in O2 consumption (OC) can be used to this aim, as they provide options for improved surgical, image-guided approaches.

Methods

In the present study, the potential of a fluorescent sensor foil-based technology to quantify OC in HNSCC was evaluated in an in vitro 3D model and in situ in patients.

Results

In vitro measurements of OC using hypopharyngeal and esophageal cell lines allowed a specific detection of tumor cell spheroids embedded together with cancer-associated fibroblasts in type I collagen extracellular matrix down to a diameter of 440 µm. Pre-surgery in situ measurements were conducted with a handheld recording device and sensor foils with an oxygen permeable membrane and immobilized O2-reactive fluorescent dyes. Lateral tongue carcinoma and carcinoma of the floor of the mouth were chosen for analysis owing to their facilitated accessibility. OC was evaluated over a time span of 60 seconds and was significantly higher in tumor tissue compared to healthy mucosa in the vicinity of the tumor.

Discussion

Hence, OC quantification using fluorescent sensor foil-based technology is a relevant parameter for the differentiation of tumor tissue of the head and neck region and may support surgery planning.

Optical imaging for screening and early cancer diagnosis in low-resource settings

Low-cost optical imaging technologies have the potential to reduce inequalities in healthcare by improving the detection of pre-cancer or early cancer and enabling more effective and less invasive treatment. In this Review, we summarise technologies for in vivo widefield, multi-spectral, endoscopic, and high-resolution optical imaging that could offer affordable approaches to improve cancer screening and early detection at the point-of-care. Additionally, we discuss approaches to slide-free microscopy, including confocal imaging, lightsheet microscopy, and phase modulation techniques that can reduce the infrastructure and expertise needed for definitive cancer diagnosis. We also evaluate how machine learning-based algorithms can improve the accuracy and accessibility of optical imaging systems and provide real-time image analysis. To achieve the potential of optical technologies, developers must ensure that devices are easy to use; the optical technologies must be evaluated in multi-institutional, prospective clinical tests in the intended setting; and the barriers to commercial scale-up in under-resourced markets must be overcome. Therefore, test developers should view the production of simple and effective diagnostic tools that are accessible and affordable for all countries and settings as a central goal of their profession.

Salivary metabolomics for oral leukoplakia with and without dysplasia

PURPOSE

Oral leukoplakia (OL) is a common potentially malignant oral disorder. Therefore, there is a need for simple screening methods for OL before its transformation into oral cancer. Furthermore, because invasive open biopsy is the sole method to determine if an OL lesion is dysplastic, there is also a clinical need for non-invasive methods to differentiate dysplastic OL from non-dysplastic OL. This study aimed to identify salivary metabolites that can help differentiate patients with OL from healthy controls (HC) and also dysplastic OL from non-dysplastic OL.

MATERIAL & METHODS

Whole unstimulated saliva samples were collected from patients with OL (n = 30) and HCs (n = 29). The OL group included nine patients with dysplastic OL and 20 with non-dysplastic OL. Hydrophilic metabolites in the saliva samples were comprehensively analyzed through capillary electrophoresis mass spectrometry. To evaluate the discrimination ability of a combination of multiple markers, a multiple logistic regression (MLR) model was developed to differentiate patients with OL from HCs and dysplastic OL from non-dysplastic OL.

RESULTS

Twenty-eight metabolites were evidently different between patients with OL and HCs. Finally, three metabolites (guanine, carnitine, and N-acetylputrescine) were selected to develop the MLR model, which resulted in a high area under curve (AUC) of the receiver operating characteristic (ROC) to differentiate patients with OL from HCs (AUC = 0.946, p < 0.001, 95% confidential interval [CI] = 0.889- 1.000). Similarly, two metabolites were evidently different between patients with dysplastic and non-dysplastic OL. Finally, only one metabolite (7-methylguanine) was selected in the MLR model, which revealed a moderate discrimination ability for dysplastic and non-dysplastic OL (AUC = 0761, p = 0.027, 95% CI = 0.551-0.972).

CONCLUSION

Our candidate salivary metabolites showed potential not only to discriminate OL from HC, but also to discriminate dysplastic OL from non-dysplastic OL.

Status and Determinants of Early Detection of Oral Premalignant and Malignant Lesions in India

It has been over four decades since the launch of the National Cancer Control Programme in India, yet the cancer screening rates for oral cancer remain unremarkable. Moreover, India is bracing a large burden of oral cancer with poor survival rates. An effective public health programme implementation relies on a multitude of factors related to cost-effective evidence-based interventions, the healthcare delivery system, public health human resource management, community behaviour, partnership with stakeholders, identifying opportunities and political commitment. In this context, we discuss the various challenges in the early detection of oral premalignant and malignant lesions and potential solutions.