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.

Predicting oral cancer survival-Development and validation of an Asia-Pacific nomogram.

BACKGROUND: Nomograms are graphical calculating devices that predict response to treatment during cancer management. Oral squamous cell carcinoma (OSCC) is a lethal and deforming disease of rising incidence and global significance. The aim of this study was to develop a nomogram to predict individualized OSCC survival using a population-based dataset obtained from Queensland, Australia and externally validated using a cohort of OSCC patients treated in Hong Kong. METHODS: Clinico-pathological data for newly diagnosed OSCC patients, including age, sex, tumour site and grading, were accessed retrospectively from the Queensland Cancer Registry (QCR) in Australia and the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong. Multivariate Cox proportional hazard regression was used to construct overall survival (OS) and cancer-specific survival (CSS) prediction models. Nomograms were internally validated using 10-fold cross validation, and externally validated against the Hong Kong dataset. RESULTS: Data from 9885 OSCC patients in Queensland and 465 patients from Hong Kong were analysed. All clinico-pathological variables significantly influenced survival outcomes. Nomogram calibration curves demonstrated excellent agreement between predicted and actual probability for Queensland patients. External validation in the Hong Kong population demonstrated slightly poorer nomogram performance, but predictive power remained strong. CONCLUSION: Based upon readily available data documenting patient demographic and clinico-pathological variables, predictive nomograms offer pragmatic aid to clinicians in individualized treatment planning and prognosis assessment in contemporary OSCC management.

Oral cancer in Australia: Rising incidence and worsening mortality.

BACKGROUND: Oral cancer, predominantly squamous cell carcinoma (SCC), is a lethal and deforming disease of rising incidence. Although largely preventable by eliminating harmful tobacco and alcohol risk factor behaviour, 5-year survival rates remain around 50%, primarily due to late presentation of advanced stage disease. Whilst low socio-economic status, regional and remote location and indigenous status are associated with head and neck cancer in general, detailed incidence and demographic data for oral SCC in Australia are limited. This study aimed to characterise the Queensland population at risk of oral SCC development. METHODS: Following ethical approval, the Queensland Cancer Register (QCR) dataset was analysed to determine patterns of incidence, anonymised patient demographics, clinical presentation and outcome data for oral SCC cases diagnosed between 1982 and 2018. RESULTS: Data from 9887 patients were obtained. Mean age at diagnosis was 64.55 years, with a male-to-female ratio of 2.51:1; males were diagnosed at a younger age (p < 0.001). At study census date, 59% of patients had died, with females demonstrating longer mean survival (p < 0.001). Clinicopathological data confirmed that SCC most commonly arose from tongue sites (49%) and, whilst tumours were predominantly moderately differentiated in nature (63%), patients with poorly differentiated carcinomas exhibited shortest survival times (p < 0.05). Over the 36-year study period, the number of diagnoses increased 4.49-fold, whilst the number of deaths increased 19.14-fold. CONCLUSION: Oral SCC poses a significant and growing healthcare problem in Queensland. In the absence of national screening, characterising the high-risk oral SCC population facilitates pragmatic opportunities to raise disease awareness, to deliver targeted screening and effective primary prevention strategies, and to provide early interventional treatment intervention to reduce disease mortality and morbidity.

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.

Local delivery of hydrogel encapsulated vascular endothelial growth factor for the prevention of medication-related osteonecrosis of the jaw.

The anti-angiogenic effects of bisphosphonates have been hypothesized as one of the major etiologic factors in the development of medication-related osteonecrosis of the jaw (MRONJ), a severe debilitating condition with limited treatment options. This study evaluated the potential of a gelatine-hyaluronic acid hydrogel loaded with the angiogenic growth factor, vascular endothelial growth factor (VEGF), as a local delivery system to aid in maintaining vascularization in a bisphosphonate-treated (Zoledronic Acid) rodent maxillary extraction defect. Healing was assessed four weeks after implantation of the VEGF-hydrogel into extraction sockets. Gross examination and histological assessment showed that total osteonecrosis and inflammatory infiltrate was significantly reduced in the presence of VEGF. Also, total vascularity and specifically neovascularization, was significantly improved in animals that received VEGF hydrogel. Gene expression of vascular, inflammatory and bone specific markers within the defect area were also significantly altered in the presence of VEGF. Furthermore, plasma cytokine levels were assessed to determine the systemic effect of locally delivered VEGF and showed similar outcomes. In conclusion, the use of locally delivered VEGF within healing extraction sockets assists bone healing and prevents MRONJ via a pro-angiogenic and immunomodulatory mechanism.