Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders.

Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks. Artificial intelligence (AI) has the potential to integrate disparate factors influencing malignant transformation for robust, precise, and personalized cancer risk stratification of OPMD patients than current methods to determine the need for surgical resection, excision, or re-excision. Therefore, this article overviews existing AI models and tools, presents a clinical implementation pathway, and discusses necessary refinements to aid the clinical application of AI-based platforms for cancer risk stratification of OPMDs in surgical practice.

Artificial intelligence in salivary biomarker discovery and validation for oral diseases.

Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease-related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi-omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.

Bayesian Disease Mapping to Identify High-Risk Population for Oral Cancer: A Retrospective Spatiotemporal Analysis.

Objectives: Bayesian mapping is an effective spatiotemporal approach to identify high-risk geographic areas for diseases and has not been used to identify oral cancer hotspots in Australia previously. This retrospective disease mapping study was undertaken to identify the oral cancer trends and patterns within the Queensland state in Australia. Methods: This study included data obtained from Queensland state Cancer Registry from 1982 to 2018. Domains mapped included the oral cancer incidence and mortality in Queensland (QLD). Local government areas (LGAs) and suburbs were utilized as geographical units for the estimation using Bayesian mapping approach. Results: Of the 78 LGAs, 21 showed high-oral cancer incidence as measured using higher median smoothed incidence risk (SIR), above the state average. Specifically, nine LGAs within predominantly rural areas had SIR above 100% of the state average. Of these, only one LGA (Mount Isa City) had a median smoothed SIR and 95% CI of 2.61 (2.14-3.15) which was constantly above 100% of the state average. Furthermore, mortality risk estimated using smoothed mortality risk (SMR), were significantly higher than the state average in 31 LGAs. Seventeen LGAs had a median SMR above 100% of the state average while three LGAs had the highest overall, 3- and 5-year mortality risks. Considering the 95% credible interval which is indicative of the uncertainty around the estimates, three LGAs had the highest overall mortality risks-Yarrabah Aboriginal Shire (3.80 (2.16-6.39)), Cook Shire (3.37 (2.21-5.06)), and Mount Isa City (3.04 (2.40-3.80)). Conclusion: Bayesian disease mapping approach identified multiple incidence and mortality hotspots within regional areas of the Queensland. Findings from our study can aid in designing targeted public health screening and interventions for primary prevention of oral cancer in regional and remote communities.

Validity of nomograms for predicting cancer risk in oral leukoplakia and oral lichen planus.

OBJECTIVES: This study assessed the validity of nomograms for predicting malignant transformation (MT) among patients with oral leukoplakia (OL) and oral lichen planus (OLP). MATERIALS AND METHODS: Two nomograms were identified following a systematic search. Variables to interrogate both nomograms were obtained for a retrospective OL/OLP cohort. Then, the nomograms were applied to estimate MT probabilities twice and their average was used to calculate the discriminatory performance, calibration, and potential net benefit of the models. Subgroup analyses were performed for patients with OL, OLP, and oral epithelial dysplasia. RESULTS: Predicted probabilities were mostly significantly higher among OL/OLP patients who developed MT compared to those who did not (p = <0.001-0.034). AUC values and Brier scores of the nomograms were 0.644-0.844 and 0.040-0.088 among OL patients and 0.580-0.743 and 0.008-0.072 among OLP patients. Decision curve analysis suggested that the nomograms had some net benefit for risk stratification. However, the models did not best binary dysplasia grading in discriminatory validity and net benefit among patients with OL and oral epithelial dysplasia. CONCLUSION: Nomograms for predicting MT may have satisfactory validity among patients with OL than OLP, but they do not outperform binary dysplasia grading in risk stratification of OL.

The quorum-sensing peptidic inhibitor rescues host immune system eradication: A novel infectivity mechanism.

Subverting the host immune system is a major task for any given pathogen to assure its survival and proliferation. For the opportunistic human pathogen Bacillus cereus (Bc), immune evasion enables the establishment of potent infections. In various species of the Bc group, the pleiotropic regulator PlcR and its cognate cell-cell signaling peptide PapR7 regulate virulence gene expression in response to fluctuations in population density, i.e., a quorum-sensing (QS) system. However, how QS exerts its effects during infections and whether PlcR confers the immune evading ability remain unclear. Herein, we report how interception of the QS communication in Bc obliterates the ability to affect the host immune system. Here, we designed a peptide-based QS inhibitor that suppresses PlcR-dependent virulence factor expression and attenuates Bc infectivity in mouse models. We demonstrate that the QS peptidic inhibitor blocks host immune system-mediated eradication by reducing the expression of PlcR-regulated major toxins similarly to the profile that was observed for isogenic strains. Our findings provide evidence that Bc infectivity is regulated by QS circuit-mediated destruction of host immunity, thus reveal a interesting strategy to limit Bc virulence and enhance host defense. This peptidic quorum-quenching agent constitutes a readily accessible chemical tool for studying how other pathogen QS systems modulate host immunity and forms a basis for development of anti-infective therapeutics.

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.

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.

Background: The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs. Methods: PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study. Results: ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy. Conclusion: Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designs. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.

Predicting Overall Survival Using Machine Learning Algorithms in Oral Cavity Squamous Cell Carcinoma.

BACKGROUND/AIM: Machine learning (ML) models are often modelled to predict cancer prognosis but rarely consider spatial factors in a region. Hence this study explored machine learning algorithms utilising Local Government Areas (LGAs) in Queensland, Australia to spatially predict 3- and 5-year prognosis of oral cancer patients and provide clinical interpretability of the predicted outcome made by the ML model. PATIENTS AND METHODS: Data from a total of 3,841 oral cancer patients were retrieved from the Queensland Cancer Registry (QCR). Synthesizing minority oversampling technique together with edited nearest neighbours (SMOTE-ENN) was used to pre-process unbalanced datasets. Five ML models: logistic regression, random forest classifier, XGBoost, Gaussian Naïve Bayes and Voting Classifier were trained. Predictive features were age, sex, LGAs, tumour site and differentiation. Outcomes were 3- and 5-year overall survival of patients. Model performances on test set were evaluated using area under the curve and F1 scores. SHapley Additive exPlanations (SHAP) method was applied to the best performing model for model interpretation of the predicted outcome. RESULTS: The Voting Classifier was the best performing model with F1 score of 0.58 and 0.64 for 3- and 5-year overall survival, respectively. Age was the most important feature in the Voting Classifier in 3- and 5-year prognosis prediction. LGAs at diagnosis was the top 3 predictive feature for both 3- and 5-year models. CONCLUSION: The Voting Classifier demonstrated the best overall performance in classifying both 3- and 5-year overall survival of oral cancer patients in Queensland. SHAP method provided clinical understanding of the predictive features of the Voting Classifier.

Machine Learning-Based Genome-Wide Salivary DNA Methylation Analysis for Identification of Noninvasive Biomarkers in Oral Cancer Diagnosis.

This study aims to examine the feasibility of ML-assisted salivary-liquid-biopsy platforms using genome-wide methylation analysis at the base-pair and regional resolution for delineating oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). A nested cohort of patients with OSCC and OPMDs was randomly selected from among patients with oral mucosal diseases. Saliva samples were collected, and DNA extracted from cell pellets was processed for reduced-representation bisulfite sequencing. Reads with a minimum of 10× coverage were used to identify differentially methylated CpG sites (DMCs) and 100 bp regions (DMRs). The performance of eight ML models and three feature-selection methods (ANOVA, MRMR, and LASSO) were then compared to determine the optimal biomarker models based on DMCs and DMRs. A total of 1745 DMCs and 105 DMRs were identified for detecting OSCC. The proportion of hypomethylated and hypermethylated DMCs was similar (51% vs. 49%), while most DMRs were hypermethylated (62.9%). Furthermore, more DMRs than DMCs were annotated to promoter regions (36% vs. 16%) and more DMCs than DMRs were annotated to intergenic regions (50% vs. 36%). Of all the ML models compared, the linear SVM model based on 11 optimal DMRs selected by LASSO had a perfect AUC, recall, specificity, and calibration (1.00) for OSCC detection. Overall, genome-wide DNA methylation techniques can be applied directly to saliva samples for biomarker discovery and ML-based platforms may be useful in stratifying OSCC during disease screening and monitoring.

Performance of a simplified scoring system for risk stratification in oral cancer and oral potentially malignant disorders screening.

BACKGROUND: Impact and efficiency of oral cancer and oral potentially malignant disorders screening are most realized in "at-risk" individuals. However, tools that can provide essential knowledge on individuals' risks are not applied in risk-based screening. This study aims to optimize a simplified risk scoring system for risk stratification in organized oral cancer and oral potentially malignant disorders screening. METHODS: Participants were invited to attend a community-based oral cancer and oral potentially malignant disorders screening program in Hong Kong. Visual oral examination was performed for all attendees and information on sociodemographic characteristics as well as habitual, lifestyle, familial, and comorbidity risk factors were obtained. Individuals' status of those found to have suspicious lesions following biopsy and histopathology were classified as positive/negative and this outcome was used in a multiple logistic regression analysis with variables collected during screening. Odds ratio weightings were then used to develop a simplified risk scoring system which was validated in an external cohort. RESULTS: Of 979 participants, 4.5% had positive status following confirmatory diagnosis. A 12-variable simplified risk scoring system with weightings was generated with an AUC, sensitivity, and specificity of 0.82, 0.71, and 0.78 for delineating high-risk cases. Further optimization on the validation cohort of 491 participants yielded a sensitivity and specificity of 0.75 and 0.87 respectively. CONCLUSIONS: The simplified risk scoring system was able to stratify oral cancer and oral potentially malignant disorders risk with satisfactory sensitivity and specificity and can be applied in risk-based disease screening.

Efficacy of hypermethylated DNA biomarkers in saliva and oral swabs for oral cancer diagnosis: Systematic review and meta-analysis.

OBJECTIVES: This study aims to determine the diagnostic test accuracy (DTA) of hypermethylated DNA biomarkers in saliva and oral swabs for oral squamous cell carcinoma (OSCC) detection from the prevalidation studies available. MATERIALS AND METHODS: Electronic database searching of PubMed, EMBASE, Cochrane Library, Scopus, Web of Science, and LILACS was conducted to identify relevant articles that were published between January 1, 2000, and August 1, 2020. RESULTS: Meta-analysis was conducted based on 11 of 20 studies selected for review. Included studies had high bias concerns on the QUADAS-2 study assessment tool. We found that salivary and oral swab hypermethylation markers had better specificity than sensitivity for oral cancer detection. Summary sensitivity and specificity (95% CI) of hypermethylation panels were 86.2% (60-96.2) and 90.6% (85.9-93.9) while for individual markers, summary sensitivity and specificity (95% CI) were 70% (56.9-80.5) and 91.9% (80.3-96.9), respectively. Respective positive and negative likelihood ratios for combined markers were 9.2 (5.89-14.36) and 0.15 (0.05-0.5), and 8.61 (3.39-21.87) and 0.33 (0.22-0.49) for single-application biomarkers. CONCLUSION: DNA hypermethylation biomarkers especially in combination have acceptable DTA that warrants further optimization with rigorous biomarker evaluation methods for conclusive determination of their efficacy.

Oral Cancer Awareness and Individuals' Inclination to Its Screening and Risk Prediction in Hong Kong.

Assessing the baseline knowledge status and expectations of the target population of any health promotion and secondary prevention program is essential to the success of such intervention. To obtain this information about the Hong Kong population a priori to implementing these preventive strategies for oral cancer in addition to determining the willingness of potential screening participants to take risk-profiling assessments, a cross-sectional survey was conducted between November 2019 and March 2020. A total of 964 residents between the ages 18 and 86 years were invited to participate in this study across the three geographical areas in Hong Kong. Most participants self-reported being aware of oral cancer (86.3%), although the proportion of those with substantial knowledge on salient risk factors and early identifiable signs were very low (2.9%). Age and level of education were the only demographic characteristics associated with the knowledge status. The proportion of participants willing to attend community screening and partake in risk profiling assessment was high (83.9% and 80.9% respectively). Willingness to attend community screening was directly associated with respondents' self-reported oral cancer awareness status (OR: 1.9, 95% CI: 1.22-2.96). Also, we observed that those participants who were willing to attend screening are more inclined to take risk prediction assessments that those not willing to attend. These findings have showcased the need to intensify health promotion via personal skills development to encourage early disease presentation and will assist in the planning of these programs accordingly in the Hong Kong population.

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.

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

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.

Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review.

OBJECTIVES: Machine learning platforms are now being introduced into modern oncological practice for classification and prediction of patient outcomes. To determine the current status of the application of these learning models as adjunctive decision-making tools in oral cavity cancer management, this systematic review aims to summarize the accuracy of machine-learning based models for disease outcomes. METHODS: Electronic databases including PubMed, Scopus, EMBASE, Cochrane Library, LILACS, SciELO, PsychINFO, and Web of Science were searched up until December 21, 2020. Pertinent articles detailing the development and accuracy of machine learning prediction models for oral cavity cancer outcomes were selected in a two-stage process. Quality assessment was conducted using the Quality in Prognosis Studies (QUIPS) tool and results of base studies were qualitatively synthesized by all authors. Outcomes of interest were malignant transformation of precancer lesions, cervical lymph node metastasis, as well as treatment response, and prognosis of oral cavity cancer. RESULTS: Twenty-seven articles out of 950 citations identified from electronic and manual searching were included in this study. Five studies had low bias concerns on the QUIPS tool. Prediction of malignant transformation, cervical lymph node metastasis, treatment response, and prognosis were reported in three, six, eight, and eleven articles respectively. Accuracy of these learning models on the internal or external validation sets ranged from 0.85 to 0.97 for malignant transformation prediction, 0.78-0.91 for cervical lymph node metastasis prediction, 0.64-1.00 for treatment response prediction, and 0.71-0.99 for prognosis prediction. In general, most trained algorithms predicting these outcomes performed better than alternate methods of prediction. We also found that models including molecular markers in training data had better accuracy estimates for malignant transformation, treatment response, and prognosis prediction. CONCLUSION: Machine learning algorithms have a satisfactory to excellent accuracy for predicting three of four oral cavity cancer outcomes i.e., malignant transformation, nodal metastasis, and prognosis. However, considering the training approach of many available classifiers, these models may not be streamlined enough for clinical application currently.

"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.

The Tale of the Unrepaired Cleft-Retrospective Evaluation of Cases Encountered by an Indigenous Mission in a Northern Nigerian Community.

OBJECTIVE: This study aims to document the experience of an indigenous surgical mission on the occurrence of unrepaired cleft in 2 visits to Minna, North-Central Nigeria. DESIGN: This retrospective study involved participants with orofacial cleft anomaly at 2 surgical outreaches held in Minna in 2011 and 2017. Baseline data were initially obtained from case files of patients at both programs. Data collected were analyzed employing appropriate statistical tests for continuous and categorical variables. SETTING: Two outreach programs in Minna, North-Central Nigeria by Cleft and Facial Deformity Foundation in 2011 and 2017. RESULTS: A total of 117 participants with cleft anomaly were encountered at both surgical outreach programs. The sample prevalence of unrepaired cleft was 61.5% with an overall mean age (standard deviation) of 10 (13.2) years. Most participants presented with unilateral complete cleft lip (70.8%) which was more common on the left side and had no family history of orofacial cleft (54.2%). Information on the surgical program was mostly obtained via friends and relatives in 32.6% and lack of wherewithal to offset the expense of cleft surgery and supportive treatment represented the most common reason for the delay of surgical repair (50%). CONCLUSION: We found a high proportion of patients with unrepaired cleft in our sample which may mirror happenings in other developing world centers. We advocate continued collaborations between indigenous missions and international funding agencies to further encourage continued repair of unrepaired cleft in developing centers.

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

OBJECTIVES: To compare the treatment response and prognosis of oral cavity cancer between non-smoking and non-alcohol-drinking (NSND) patients and smoking and alcohol-drinking (SD) patients. METHODS: A total of 313 consecutively treated patients from 2000 to 2019 were included. Demographic, clinicopathologic, treatment, and prognosis information were obtained. Relapse-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) were compared between NSND and SD groups using Kaplan-Meier plots, log-rank test, and multivariate Cox regression analysis. RESULTS: Sample prevalence of NSND patients was 54.6%. These patients were predominantly females in their eighth decade with lower prevalence of floor of the mouth cancers compared to SD patients (1.8% vs 14.8%). No difference in the RFS and DSS between both groups was found following multivariable analysis; however, NSND patients had better OS (HR (95% CI) - 0.47 (0.29-0.75); p = 0.002). Extracapsular extension was associated with significantly poorer OS, DSS, and RFS in this oral cavity cancer cohort. CONCLUSION: Treatment response and disease-specific prognosis are comparable between NSND and SD patients with oral cavity cancer. However, NSND patients have better OS. CLINICAL RELEVANCE: This study shows that oral cavity cancer in NSND is not less or more aggressive compared to SD patients. Although better survival is expected for NSND than SD patients, this is likely due to the reduced incidence of other chronic diseases in the NSND group.