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.
SUBJECTS AND METHODS: A prospective study of 355 participants, including 280 with oral lesions/variants was conducted. Adults aged ≥18 treated at tertiary referral centres were included. Images of the oral cavity were taken using MeMoSA®. The identification of the presence of lesion/variant and referral decision made using MeMoSA® were compared to clinical oral examination, using kappa statistics for intra-rater agreement. Sensitivity, specificity, concordance and F1 score were computed. Images were reviewed by an off-site specialist and inter-rater agreement was evaluated. Images from sequential clinical visits were compared to evaluate observable changes in the lesions.
RESULTS: Kappa values comparing MeMoSA® with clinical oral examination in detecting a lesion and referral decision was 0.604 and 0.892, respectively. Sensitivity and specificity for referral decision were 94.0% and 95.5%. Concordance and F1 score were 94.9% and 93.3%, respectively. Inter-rater agreement for a referral decision was 0.825. Progression or regression of lesions were systematically documented using MeMoSA®.
CONCLUSION: Referral decisions made through MeMoSA® is highly comparable to clinical examination demonstrating it is a reliable telemedicine tool to facilitate the identification of high-risk lesions for early management.
SIGNIFICANCE: Mutant HRAS drives metastasis of head and neck cancer by switching off the Hippo pathway to activate the YAP1-AXL axis and to stimulate lymphovascular angiogenesis.
SIGNIFICANCE: NPC268 is the first and only EBV-positive cell line derived from a primary non-keratinizing, differentiated nasopharyngeal carcinoma, an understudied but important subtype in Southeast Asian countries. This model adds to the limited number of authentic EBV-positive lines globally that will facilitate mechanistic studies and drug development for NPC.
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.