Prognostic value of non-smoking, non-alcohol drinking status in oral cavity cancer

Prevalence and risk factors of oral potentially malignant disorders in Indonesia: a cross-sectional study undertaken in 5 provinces

Detection of subjects with oral potentially malignant disorders in a population is key to early detection of oral cancer (OC) with consequent reduction of cancer-related morbidity and mortality. Our aim was to investigate the prevalence and associated risk factors for OPMD in representative provinces of Indonesia. This cross-sectional study was undertaken in five Indonesian provinces: West Java (WJ), Jakarta (JKT), West Papua (WP), West Kalimantan (WK) and Banda Aceh (BA). Respondents answered a previously validated questionnaire including information on ethnicity, occupation, socioeconomic status (SES), oral health practices, and behaviours associated with oral cancer. An oral examination was undertaken using WHO standardized methodology. Data were analysed using ANOVA, Chi-Square, and logistic regression to assess association between risk factors and mucosal disease. A total of 973 respondents between the ages of 17 and 82 years was enrolled (WJ 35.5%,JKT 13.3% WP 18.3%, WK 9%, BA 23.9%). Tobacco smoking (14.8%), Betel quid (BQ) chewing (12.6%) and alcohol drinking (4%) varied geographically. A well-established OPMD was detected in 137 (14.1%) respondents and 2 (0.2%) presented with chronic ulceration later diagnosed as OC. Leukoplakia was the most common OPMD found (9.7%), while the prevalence of oral submucous fibrosis (OSMF), not previously described in the nation, was 2.3%. Poor knowledge of OC risk factors, poor oral hygiene behaviours, low-income SES and ethnicity were significantly associated with the presence of an OPMD. There is a previously under-reported high prevalence of OPMD in Indonesia. Overall, we found a strong correlation between the presence of an OPMD and individual habituation to known risk factors.

Oral cavity cancer in young, non-smoking, and non-drinking patients: A contemporary review

Oral squamous cell carcinoma (OSCC) in non-smoking and non-drinking (NSND) individuals appears to be distinct from the traditional head and neck squamous cell carcinoma (HNSCC). The incidence of this subset is increasing, as are the number of studies examining its characteristics. NSND OSCC individuals tend to be younger (<45 years) compared to traditional HNSCC patients. The proportion of females in the NSND OSCC cohort is also higher. The tongue is the predominantly affected subsite. Studies have revealed several gene mutations and unique epigenomic profiles but no definitive genetic etiology. Transcriptomic analysis has not found any causative viral agents. Other proposed etiologies include chronic dental trauma, microbiome abnormalities, marijuana consumption, and genetic disorders. There are international efforts to determine the relative prognostic outcome of this unique cohort, but no consensus has been reached. Here, we review the incidence, demographics, subsite, possible etiologies, prognosis, and therapy implications of the NSND OSCC cohort.

Explainable ensemble learning model improves identification of candidates for oral cancer screening

OBJECTIVES

Artificial intelligence could enhance the use of disparate risk factors (crude method) for better stratification of patients to be screened for oral cancer. This study aims to construct a meta-classifier that considers diverse risk factors to identify patients at risk of oral cancer and other suspicious oral diseases for targeted screening.

MATERIALS AND METHODS

A retrospective dataset from a community oral cancer screening program was used to construct and train the novel voting meta-classifier. Comprehensive risk factor information from this dataset was used as input features for eleven supervised learning algorithms which served as base learners and provided predicted probabilities that are weighted and aggregated by the meta-classifier. Training dataset was augmented using SMOTE-ENN. Additionally, Shapley additive explanations (SHAP) values were generated to implement the explainability of the model and display the important risk factors.

RESULTS

Our meta-classifier had an internal validation recall, specificity, and AUROC of 0.83, 0.86, and 0.85 for identifying the risk of oral cancer and 0.92, 0.60, and 0.76 for identifying suspicious oral mucosal disease respectively. Upon external validation, the meta-classifier had a significantly higher AUROC than the crude/current method used for identifying the risk of oral cancer (0.78 vs 0.46; p = 0.001) Also, the meta-classifier had better recall than the crude method for predicting the risk of suspicious oral mucosal diseases (0.78 vs 0.47).

CONCLUSION

Overall, these findings showcase that our approach optimizes the use of risk factors in identifying patients for oral screening which suggests potential clinical application.

Epidemiological, Clinical, and Oncological Outcomes of non-Alcohol Drinking and non-Smoking Laryngeal Squamous Cell Carcinoma Patients: A Distinct Entity

Purpose: To explore the discrepancy in clinicopathological and prognostic features between smoking and alcohol drinking (SA) and non-smoking and non-alcohol drinking (NSNA) patients with laryngeal squamous cell carcinoma (LSCC). Methods: This retrospective study including 1735 patients with LSCC was conducted from January 2005 to December 2010, which were categorized into 4 groups, NSNA group, smoking only group, alcohol-drinking only group, and SA group. We compared overall survival (OS) and disease-free survival (DFS) using the Kaplan-Meier method and indicated clinicopathological features by Cox proportional hazards regression models before and after propensity score matching (PSM). Results: A total of 415 patients (23.92%) were identified as NSNA. The SA group was predominantly patients ≤60 years old (46.63%) while the NSNA group was more older (58.07%). NSNA group was more likely to present at earlier disease stage and more female. No significant difference in OS (P = .685) and DFS (P = .976) was found between the 2 groups. In addition to age and recurrence and metastasis being common independent prognostic factors in terms of OS in both groups of patients, NSNA group also exhibited other factors, namely tumor area >3.7 cm² and positive resection margin. For DFS, N + stage, tumor size >3.7 cm², and positive resection margin were prognostic features specific to NSNA group. Conclusion: The outcome is similar in LSCC patients with and without SA. NSNA group shows a distinct profile from that found in SA group. Clinicopathological features from NSNA group should be considered for LSCC management.

Association between smoking habits and dental care utilization and cost using administrative claims database and specific medical check-up data

BACKGROUND

This study aims to evaluate the association between smoking habits and dental care utilization and cost in individuals registered with the Japan Health Insurance Association, Osaka branch.

METHODS

We used the administrative claims database and specific medical check-up data and included 226,359 participants, who visited dental institutions, underwent dental examinations, and underwent specific medical checkups, with smoking data from April 2016 to March 2017. We calculated propensity scores with age, gender, exercise, eating habits, alcohol intake, and sleep. We also compared dental care utilization with the total cost of each procedure.

RESULTS

According to propensity score matching, 62,692 participants were selected for each group. Compared to non-smokers, smokers were younger, and a higher proportion were men. Smokers tended to skip breakfast, have dinner just before bed, and drink alcohol. After adjusting for potential confounding factors with propensity score matching, the mean annual dental cost among smokers was significantly higher than non-smokers. The prevalence of pulpitis, missing teeth, and apical periodontitis were higher among smokers than non-smokers, while inlay detachment, caries, and dentine hypersensitivity were higher among non-smokers.

CONCLUSION

This study suggests that smokers have higher dental cost consisted of progressive dental caries, missing teeth, and uncontrolled acute inflammation that necessitated the use of medications. It is suggested that smokers tend to visit the dentist after their symptoms become severe.

Efficiency of biofilm removal by combination of water jet and cold plasma: an in-vitro study

Background

Peri-implantitis therapy is a major problem in implantology. Because of challenging rough implant surface and implant geometry, microorganisms can hide and survive in implant microstructures and impede debridement. We developed a new water jet (WJ) device and a new cold atmospheric pressure plasma (CAP) device to overcome these problems and investigated aspects of efficacy in vitro and safety with the aim to create the prerequisites for a clinical pilot study with these medical devices.

Methods

We compared the efficiency of a single treatment with a WJ or curette and cotton swab (CC) without or with adjunctive use of CAP (WJ + CAP, CC + CAP) to remove biofilm in vitro from rough titanium discs. Treatment efficacy was evaluated by measuring turbidity up to 72 h for bacterial re-growth or spreading of osteoblast-like cells (MG-63) after 5 days with scanning electron microscopy. With respect to application safety, the WJ and CAP instruments were examined according to basic regulations for medical devices.

Results

After 96 h of incubation all WJ and CC treated disks were turbid but 67% of WJ + CAP and 46% CC + CAP treated specimens were still clear. The increase in turbidity after WJ treatment was delayed by about 20 h compared to CC treatment. In combination with CAP the cell coverage significantly increased to 82% (WJ + CAP) or 72% (CC + CAP), compared to single treatment 11% (WJ) or 10% (CC).

Conclusion

The newly developed water jet device effectively removes biofilm from rough titanium surfaces in vitro and, in combination with the new CAP device, biologically acceptable surfaces allow osteoblasts to grow. WJ in combination with CAP leads to cleaner surfaces than the usage of curette and cotton swabs with or without subsequent plasma treatment. Our next step will be a clinical pilot study with these new devices to assess the clinical healing process.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02195-1.

Molecular drivers of oral cavity squamous cell carcinoma in non-smoking and non-drinking patients: what do we know so far?

Oral cavity squamous cell carcinoma (OCSCC) is one of the most common head and neck cancers worldwide. It is well known that risk factors for OCSCC include tobacco and excess alcohol consumption. However, in recent years, OCSCC incidence has been increasing in patients without these traditional risk factors. The cause of this increase is unclear and various genetic, environmental, and infectious factors have been hypothesized to play a role. Additionally, there are expert opinions that oral cancer in non-smoking, non-drinking (NSND) patients have a distinct phenotype resulting in more aggressive disease presentation and poorer prognosis. In this review, we summarize the current state of knowledge for oral cavity cancer in patients without traditional risk factors.

Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders

Simple Summary

Mouth cancer is the most common malignancy in the head-and-neck region. Usually, these tumors develop from white lesions in the mouth that appear long before cancer diagnosis. However, platforms that can estimate the time-factored risk of cancer occurring from these diseases and guide treatment and monitoring approaches are elusive. To this end, our study presents time-to-event models that are based on machine learning for prediction of the risk of malignancy from oral white lesions following pathological diagnosis as a function of time. These models displayed very satisfactory discrimination and calibration after multiple tests. To facilitate their preliminary use in clinical practice and further validation, we created a website supporting the use of these models to aid decision making.

Abstract

Machine-intelligence platforms for the prediction of the probability of malignant transformation of oral potentially malignant disorders are required as adjunctive decision-making platforms in contemporary clinical practice. This study utilized time-to-event learning models to predict malignant transformation in oral leukoplakia and oral lichenoid lesions. A total of 1098 patients with oral white lesions from two institutions were included in this study. In all, 26 features available from electronic health records were used to train four learning algorithms—Cox-Time, DeepHit, DeepSurv, random survival forest (RSF)—and one standard statistical method—Cox proportional hazards model. Discriminatory performance, calibration of survival estimates, and model stability were assessed using a concordance index (c-index), integrated Brier score (IBS), and standard deviation of the averaged c-index and IBS following training cross-validation. This study found that DeepSurv (c-index: 0.95, IBS: 0.04) and RSF (c-index: 0.91, IBS: 0.03) were the two outperforming models based on discrimination and calibration following internal validation. However, DeepSurv was more stable than RSF upon cross-validation. External validation confirmed the utility of DeepSurv for discrimination (c-index—0.82 vs. 0.73) and RSF for individual survival estimates (0.18 vs. 0.03). We deployed the DeepSurv model to encourage incipient application in clinical practice. Overall, time-to-event models are successful in predicting the malignant transformation of oral leukoplakia and oral lichenoid lesions.

Comparison of time-to-event machine learning models in predicting oral cavity cancer prognosis

BACKGROUND

Applying machine learning to predicting oral cavity cancer prognosis is important in selecting candidates for aggressive treatment following diagnosis. However, models proposed so far have only considered cancer survival as discrete rather than dynamic outcomes.

OBJECTIVES

To compare the model performance of different machine learning-based algorithms that incorporate time-to-event data. These algorithms included DeepSurv, DeepHit, neural net-extended time-dependent cox model (Cox-Time), and random survival forest (RSF).

MATERIALS AND METHODS

Retrospective cohort of 313 oral cavity cancer patients were obtained from electronic health records. Models were trained on patient data following preprocessing. Predictors were based on demographic, clinicopathologic, and treatment information of the cases. Outcomes were the disease-specific and overall survival. Multivariable analyses were conducted to select significant prognostic features associated with tumor prognosis. Two models were generated per algorithm based on all-prognostic features and significant-prognostic features following statistical analysis. Concordance index (c-index) and integrated Brier scores were used as performance evaluators and model stability was assessed using intraclass correlation coefficients (ICC) calculated from these measures obtained from the cross-validation folds.

RESULTS

While all models were satisfactory, better discriminatory performance and calibration was observed for disease-specific than overall survival (mean c-index: 0.85 vs 0.74; mean integrated Brier score: 0.12 vs 0.17). DeepSurv performed best in terms of discrimination for both outcomes (c-indices: 0.76 -0.89) while RSF produced better calibrated survival estimates (integrated Brier score: 0.06 -0.09). Model stability of the algorithms varied with the outcomes as Cox-Time had the best intraclass correlation coefficient (mean ICC: 1.00) for disease-specific survival while DeepSurv was most stable for overall survival prediction (mean ICC: 0.99).

CONCLUSIONS

Machine learning algorithms based on time-to-event outcomes are successful in predicting oral cavity cancer prognosis with DeepSurv and RSF producing the best discriminative performance and calibration.

"Fact or fiction?": Oral cavity cancer in nonsmoking, nonalcohol drinking patients as a distinct entity-Scoping review

Oral cavity cancer is often described as a lifestyle-related malignancy due to its strong associations with habitual factors, including tobacco use, heavy alcohol consumption, and betel nut chewing. However, patients with no genetically predisposing conditions who do not indulge in these risk habits are still being encountered, albeit less commonly. The aim of this review is to summarize contemporaneous reports on these nonsmoking, nonalcohol drinking (NSND) patients. We performed database searching to identify relevant studies from January 1, 2000 to March 31, 2021. Twenty-six articles from 20 studies were included in this study. We found that these individuals were mostly females in their eighth decade with tumors involving the tongue and gingivobuccal mucosa. This review also observed that these patients were likely diagnosed with early stage tumors with overexpression of programmed death-ligand 1 (PD-L1) and increased intensity of tumor infiltrating lymphocytes. Treatment response and disease-specific prognosis were largely comparable between NSND and smoking/drinking patients.