MATERIALS AND METHODS: A total of 81 cases of oral cancers were matched with 162 controls in this hospital-based study. Information on sociodemographic characteristics and details of risk habits (duration, frequency and type of tobacco consumption and betel quid chewing) were collected. Association between smoking and betel quid chewing with oral cancer were analysed using conditional logistic regression.
RESULTS: Slightly more than half of the cases (55.6%) were smokers where 88.9% of them smoked kretek. After adjusting for confounders, smokers have two fold increased risk, while the risk for kretek consumers and those smoking for more than 10 years was increased to almost three-fold. Prevalence of betel quid chewing among cases and controls was low (7.4% and 1.9% respectively). Chewing of at least one quid per day, and quid combination of betel leaf, areca nut, lime and tobacco conferred a 5-6 fold increased risk.
CONCLUSIONS: Smoking is positively associated with oral cancer risk. A similar direct association was also seen among betel quid chewers.
MATERIALS AND METHODS: We developed a web-interface, hosted on a web server to collect oral lesions images from international partners. Further, we developed a customised annotation tool, also a web-interface for systematic annotation of images to build a rich clinically labelled dataset. We evaluated the sensitivities comparing referral decisions through the annotation process with the clinical diagnosis of the lesions.
RESULTS: The image repository hosts 2474 images of oral lesions consisting of oral cancer, oral potentially malignant disorders and other oral lesions that were collected through MeMoSA® UPLOAD. Eight-hundred images were annotated by seven oral medicine specialists on MeMoSA® ANNOTATE, to mark the lesion and to collect clinical labels. The sensitivity in referral decision for all lesions that required a referral for cancer management/surveillance was moderate to high depending on the type of lesion (64.3%-100%).
CONCLUSION: This is the first description of a database with clinically labelled oral lesions. This database could accelerate the improvement of AI algorithms that can promote the early detection of high-risk oral lesions.
METHODS: A mixed-methods approach was employed triangulating findings from a survey and focus groups. The survey was conducted among seven representative members of the Asia Pacific Oral Cancer Network (APOCNET) across six countries. Focus groups were conducted to gain deeper insights into the findings of the survey.
RESULTS: The identified barriers were a lack of national cancer control strategies and cancer registries and the limited availability of trained health care professionals. Overcoming these challenges in the Asia Pacific region where resources are scarce will require collaborative partnerships in data collection and novel approaches for continuous professional training including eLearning. Further, to overcome the lack of trained health care professionals, innovative approaches to the management of oral potentially malignant lesions and oral cancer including telemedicine were suggested.
CONCLUSION: The findings of this study should be taken into account when charting national cancer control plans for oral cancer and will form the basis for future collaborative studies in evaluating effective measures to improve oral cancer detection and management in low- and middle-income countries.