The potential carcinogenicity of orthopaedic implants - a scoping review.

Background: Every year, hundreds of thousands of patients receive an orthopaedic or dental implant containing metals such as cobalt, chromium and titanium. Since the European Chemicals Agency (2020) classified pure cobalt metal as a Category 1B carcinogen, manufacturers of products containing ≥ 0.1% of this metal must perform a risk assessment and justify that there are no viable alternatives.

Preoperative weight loss interventions before total hip and knee arthroplasty: a systematic review of randomized controlled trials.

Background: The high co-prevalence of obesity and end-stage osteoarthritis requiring arthroplasty, with the former being a risk factor for complications during arthroplasty, has led to increasing interest in employing preoperative weight loss interventions such as bariatric surgery and diet modification. However, the current evidence is conflicting, and this study aimed to investigate the effect of weight loss intervention before arthroplasty in prospective randomized controlled trials.

Methods: Four electronic databases (MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials) were searched for prospective randomized controlled trials that compared weight loss interventions with usual care from inception to October 2023 by following the PRISMA guidelines.

Cancer-Causing Effects of Orthopaedic Metal Implants in Total Hip Arthroplasty.

Background: Metal implants have been preferentially used in THA due to its biocompatibility, mechanical stability and durability. Yet concerns have emerged regarding their potential to release metallic ions, leading to long-term adverse effects, including carcinogenicity. This study aimed to investigate the risk of cancer development in patients with orthopaedic metal implants in total hip arthroplasty (THA).

Circulating Tumour Cells in the Prediction of Bone Metastasis.

Bone is the most common organ for the development of metastases in many primary tumours, including those of the breast, prostate and lung. In most cases, bone metastasis is incurable, and treatment is predominantly palliative. Much research has focused on the role of Circulating Tumour Cells (CTCs) in the mechanism of metastasis to the bone, and methods have been developed to isolate and count CTCs from peripheral blood.

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.

Oral cavity and oropharyngeal carcinoma disparities in age and survival in Indigenous and non-Indigenous populations of Queensland.

Objectives: To investigate the risk and prognosis of oral squamous cell carcinoma (SCC) between Indigenous and non-Indigenous populations of Queensland.

Materials And Methods: Retrospective analysis of data from the Queensland Cancer Registry (QCR) between the years 1982-2018. Main outcome measures were age at diagnosis and cumulative survival to compare the risk and prognosis of oral SCC between the populations.

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.

Influence of time between surgery and adjuvant radiotherapy on prognosis for patients with head and neck squamous cell carcinoma: A systematic review.

The timing of postoperative radiotherapy following surgical intervention in patients with head and neck cancer remains a controversial issue. This review aims to summarize findings from available studies to investigate the influence of time delays between surgery and postoperative radiotherapy on clinical outcomes. Articles between 1 January 1995 and 1 February 2022 were sourced from PubMed, Web of Science, and ScienceDirect.

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.

The Impact of Surgery on Circulating Malignant Tumour Cells in Oral Squamous Cell Carcinoma.

Importance: The extent to which surgical management of oral squamous cell carcinoma (OSCC) disseminates cancer is currently unknown.

Objective: To determine changes in numbers of malignant cells released into systemic circulation immediately following tumour removal and over the first seven post-operative days.

Design: An observational study from March 2019 to February 2021.

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.

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).

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.

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.

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.

A comparison of two methods for the detection of circulating tumour cells in patients with oral cavity cancer.

Background: Circulating tumour cells (CTCs) detected in patient blood samples are relevant as diagnostic and prognostic markers offering insights into tumour behaviour and guiding treatment of cancer at an individualised level. The aim of this study was to ascertain the feasibility of detecting CTCs in oral squamous cell carcinoma (OSCC) using two different methods so as to determine the optimal method for the study of this cancer.

Methods: Comparison of the numbers of CTCs, circulating tumour micro-emboli (CTMs) and circulating tumour endothelial cells (CTECs), was undertaken in forty clinical samples of oral squamous cell carcinoma (OSCC) determined by filtration (ISET ) and in situ fluorescent immunostaining (i-FISH, Cytelligen ) immunostaining and in situ hybridisation.

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.

Deep Learning for Clinical Image Analyses in Oral Squamous Cell Carcinoma: A Review.

Importance: Oral squamous cell carcinoma (SCC) is a lethal malignant neoplasm with a high rate of tumor metastasis and recurrence. Accurate diagnosis, prognosis prediction, and metastasis detection can improve patient outcomes. Deep learning for clinical image analysis can be used for diagnosis and prognosis in cancers, including oral SCC; its use in these areas can improve patient care and outcome.

"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.

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.