Squamous cell carcinomas of the upper aerodigestive tract are among the most common cancers worldwide, with the oral cavity being the most frequent site. Oral cavity squamous cell carcinomas (OCSCC) represent a major cause of morbidity and mortality, mainly due to high rates of locoregional or metastatic recurrence and the frequent occurrence of second primary tumors. Unlike oropharyngeal squamous cell carcinomas, human papillomavirus (HPV) is not involved in the carcinogenesis of OCSCC. In some cases, OCSCC develop from oral potentially malignant disorders (OPMDs), such as leukoplakia and erythroplakia, which have a worldwide incidence of 3-5%. The malignant transformation rate of OPMDs ranges from 3% to 50%, reflecting their marked heterogeneity. Although several clinical, histological, and molecular factors have been proposed to identify patients at high risk of malignant transformation, none have demonstrated sufficient clinical utility to date. In other cases, OCSCC arise from clinically normal oral mucosa in patients with OPMDs located at a distance and/or with established risk factors, particularly tobacco and alcohol use. Currently, no chemopreventive or preventive strategy has been established as a standard of care to prevent malignant transformation of OPMDs. Improving the prognosis of OCSCC therefore requires the development of tools to better identify high-risk OPMDs and to enable the earliest possible diagnosis. Early detection of OPMDs is essential for secondary prevention of OCSCC. However, conventional oral examination based on visual inspection and palpation has limited sensitivity, and clinical recognition of OPMDs remains challenging. Consequently, there is a clear need for improved methods to enhance early detection and risk stratification of OPMDs. Main objective: To develop a tool to aid in the diagnosis of cancerous lesions in the oral cavity using Artificial Intelligence (AI). This tool appears promising in meeting the current needs of the oral cavity practitioner community.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Develop a tool to aid in the diagnosis of cancerous lesions in the oral cavity using Artificial Intelligence (AI)
Timeframe: Through study completion, an average of 9 months