Development of a cytology-based multivariate analytical risk index for oral cancer

A brief review of cytology in dentistry

Oral cytology is a non-invasive adjunctive diagnostic tool with a number of potential applications in the practice of dentistry. This brief review begins with a history of cytology in medicine and how cytology was initially applied in oral medicine. A description of the different technical aspects of oral cytology is provided, including the collection and processing of oral cytological samples, and the microscopic interpretation and reporting, along with their advantages and limitations. Applications for oral cytology are listed with a focus on the triage of patients presenting with oral potentially malignant disorders and oral mucosal infections. Furthermore, the utility of oral cytology roles across both expert (for example, secondary oral medicine or tertiary head and neck oncology services) and non-expert (for example, primary care general dental practice) clinical settings is explored. A detailed section covers the evidence-base for oral cytology as a diagnostic adjunctive technique in both the early detection and monitoring of patients with oral cancer and oral epithelial dysplasia. The review concludes with an exploration of future directions, including the integration of artificial intelligence for automated analysis and point of care 'smart diagnostics', thereby offering some insight into future opportunities for a wider application of oral cytology in dentistry.

Diagnostic tests for oral cancer and potentially malignant disorders in patients presenting with clinically evident lesions

BACKGROUND

Squamous cell carcinoma is the most common form of malignancy of the oral cavity, and is often proceeded by oral potentially malignant disorders (OPMD). Early detection of oral cavity squamous cell carcinoma (oral cancer) can improve survival rates. The current diagnostic standard of surgical biopsy with histology is painful for patients and involves a delay in order to process the tissue and render a histological diagnosis; other diagnostic tests are available that are less invasive and some are able to provide immediate results. This is an update of a Cochrane Review first published in 2015.

OBJECTIVES

Primary objective: to estimate the diagnostic accuracy of index tests for the detection of oral cancer and OPMD, in people presenting with clinically evident suspicious and innocuous lesions.

SECONDARY OBJECTIVE

to estimate the relative accuracy of the different index tests.

SEARCH METHODS

Cochrane Oral Health's Information Specialist searched the following databases: MEDLINE Ovid (1946 to 20 October 2020), and Embase Ovid (1980 to 20 October 2020). The US National Institutes of Health Ongoing Trials Register (ClinicalTrials.gov) and the World Health Organization International Clinical Trials Registry Platform were also searched for ongoing trials to 20 October 2020. No restrictions were placed on the language or date of publication when searching the electronic databases. We conducted citation searches, and screened reference lists of included studies for additional references.

SELECTION CRITERIA

We selected studies that reported the diagnostic test accuracy of the following index tests when used as an adjunct to conventional oral examination in detecting OPMD or oral cavity squamous cell carcinoma: vital staining (a dye to stain oral mucosa tissues), oral cytology, light-based detection and oral spectroscopy, blood or saliva analysis (which test for the presence of biomarkers in blood or saliva).

DATA COLLECTION AND ANALYSIS

Two review authors independently screened titles and abstracts for relevance. Eligibility, data extraction and quality assessment were carried out by at least two authors, independently and in duplicate. Studies were assessed for methodological quality using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis was used to combine the results of studies for each index test using the bivariate approach to estimate the expected values of sensitivity and specificity.

MAIN RESULTS

This update included 63 studies (79 datasets) published between 1980 and 2020 evaluating 7942 lesions for the quantitative meta-analysis. These studies evaluated the diagnostic accuracy of conventional oral examination with: vital staining (22 datasets), oral cytology (24 datasets), light-based detection or oral spectroscopy (24 datasets). Nine datasets assessed two combined index tests. There were no eligible diagnostic accuracy studies evaluating blood or salivary sample analysis. Two studies were classed as being at low risk of bias across all domains, and 33 studies were at low concern for applicability across the three domains, where patient selection, the index test, and the reference standard used were generalisable across the population attending secondary care. The summary estimates obtained from the meta-analysis were: - vital staining: sensitivity 0.86 (95% confidence interval (CI) 0.79 to 0.90) specificity 0.68 (95% CI 0.58 to 0.77), 20 studies, sensitivity low-certainty evidence, specificity very low-certainty evidence; - oral cytology: sensitivity 0.90 (95% CI 0.82 to 0.94) specificity 0.94 (95% CI 0.88 to 0.97), 20 studies, sensitivity moderate-certainty evidence, specificity moderate-certainty evidence; - light-based: sensitivity 0.87 (95% CI 0.78 to 0.93) specificity 0.50 (95% CI 0.32 to 0.68), 23 studies, sensitivity low-certainty evidence, specificity very low-certainty evidence; and - combined tests: sensitivity 0.78 (95% CI 0.45 to 0.94) specificity 0.71 (95% CI 0.53 to 0.84), 9 studies, sensitivity very low-certainty evidence, specificity very low-certainty evidence.

AUTHORS' CONCLUSIONS

At present none of the adjunctive tests can be recommended as a replacement for the currently used standard of a surgical biopsy and histological assessment. Given the relatively high values of the summary estimates of sensitivity and specificity for oral cytology, this would appear to offer the most potential. Combined adjunctive tests involving cytology warrant further investigation. Potentially eligible studies of blood and salivary biomarkers were excluded from the review as they were of a case-control design and therefore ineligible. In the absence of substantial improvement in the tests evaluated in this updated review, further research into biomarkers may be warranted.

Development and validation of a risk model for noninvasive detection of cancer in oral potentially malignant disorders using DNA image cytometry

OBJECTIVE

To elucidate whether DNA aneuploidy was an independent discriminator for carcinoma within oral potentially malignant disorders (OPMDs), and further establish and validate a risk model based on DNA aneuploidy for the detection of oral cancer.

METHODS

A total of 810 consecutive patients with OPMD were prospectively enrolled from March 2013 to December 2018, and divided into a training set (n = 608) and a test set (n = 202). Brushing and biopsy samples from each patient were processed by DNA-DNA image cytometry and histopathological examination, respectively.

RESULTS

DNA aneuploidy of an outside DNA index ≥ 3.5 in OPMD was an independent marker strongly associated with malignant risk [adjusted odds ratio: 13.04; 95% confidence interval (CI): 5.46-31.14]. In the training and test sets, the area under the curve (AUC) was 0.87 (95% CI: 0.82-0.91) and 0.77 (95% CI: 0.57-0.97), respectively, for detecting carcinoma in OPMD patients. The independent risk factors of lateral/ventral tongue and non-homogenous type combined with a risk model built with a multivariate logistic regression revealed a more favorable diagnostic efficacy associated with the training set (AUC: 0.93; 95% CI: 0.91-0.96) and test set (AUC: 0.94; 95% CI: 0.90-0.98). The sensitivity and specificity of carcinoma detection within OPMD was improved to 100% and 88.1%, respectively.

CONCLUSIONS

This large-scale diagnostic study established a risk model based on DNA aneuploidy that consisted of a noninvasive strategy with lateral/ventral tongue and non-homogenous features. The results showed favorable diagnostic efficacy for detecting carcinoma within OPMD, irrespective of the clinical and pathological diagnoses of OPMD. Multicenter validation and longitudinal studies are warranted to evaluate community practices and clinical applications.

Oral Potentially Malignant Disorders (OPMD): What is the clinical utility of dysplasia grade?

INTRODUCTION

Oral epithelial dysplasia is considered a potential histologic precursor of subsequent squamous cell cancer. As standard clinical practice, pathologists grade dysplasia to assess risk for progression to malignancy. Except for the most advanced grade, severe dysplasia, dysplasia grading has failed to correlate well with the risk to develop invasive cancer. The questions of what process dysplasia grading best represents and what clinical utility dysplasia grading may have are explored.

AREAS COVERED

This narrative review is based on PubMed search with emphasis on papers since 2010. Epithelial dysplasia as a precursor lesion of cancer and dysplasia grading as a risk assessment tool for progression to cancer are discussed. The close clinical association of dysplasia with known carcinogens, alcohol, and tobacco products is presented.

EXPERT OPINION

Oral epithelial dysplasia is often, associated with prolonged exposure to tobacco and alcohol products. With reduction of carcinogen exposure, dysplasia is known to regress in some cases. It is proposed that histologic dysplasia grade together with macroscopic images of dysplastic clinical lesions be used as an educational tool to incentivize patients to reduce their known carcinogen exposure. This strategy has the potential to reduce lesion progression thereby reducing the disease burden of oral cancer.

Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions

BACKGROUND

The effective detection and monitoring of potentially malignant oral lesions (PMOL) are critical to identifying early-stage cancer and improving outcomes. In the current study, the authors described cytopathology tools, including machine learning algorithms, clinical algorithms, and test reports developed to assist pathologists and clinicians with PMOL evaluation.

METHODS

Data were acquired from a multisite clinical validation study of 999 subjects with PMOLs and oral squamous cell carcinoma (OSCC) using a cytology-on-a-chip approach. A machine learning model was trained to recognize and quantify the distributions of 4 cell phenotypes. A least absolute shrinkage and selection operator (lasso) logistic regression model was trained to distinguish PMOLs and cancer across a spectrum of histopathologic diagnoses ranging from benign, to increasing grades of oral epithelial dysplasia (OED), to OSCC using demographics, lesion characteristics, and cell phenotypes. Cytopathology software was developed to assist pathologists in reviewing brush cytology test results, including high-content cell analyses, data visualization tools, and results reporting.

RESULTS

Cell phenotypes were determined accurately through an automated cytological assay and machine learning approach (99.3% accuracy). Significant differences in cell phenotype distributions across diagnostic categories were found in 3 phenotypes (type 1 ["mature squamous"], type 2 ["small round"], and type 3 ["leukocytes"]). The clinical algorithms resulted in acceptable performance characteristics (area under the curve of 0.81 for benign vs mild dysplasia and 0.95 for benign vs malignancy).

CONCLUSIONS

These new cytopathology tools represent a practical solution for rapid PMOL assessment, with the potential to facilitate screening and longitudinal monitoring in primary, secondary, and tertiary clinical care settings.