ArticleCommunications medicine2025
Development and validation of a multivariable model to identify candidates for oral cancer screening in Nigeria.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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20 authors.
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Abstract
backgroundOral cancer screening can potentially improve the prevention and early detection of tumors if targeted toward at-risk individuals in the population. This study aims to profile the risk factors of oral cancer in a large Nigerian cohort to enable the selection of participants for cancer screening.
methodsThis multicenter cross-sectional study involved an organized community oral cancer screening conducted among Nigerians between April 2023 and February 2024. Visual oral examination was conducted by trained personnel to determine the presence of oral cancer and precancerous lesions among participants. Additionally, we interviewed all screened participants based on thirty risk factor information items. Multivariate analysis was performed to determine factors that are significantly associated with oral cancer and precancerous conditions, which were used to construct a multivariate predictive model for oral cancer risk stratification.
resultsScreening of 4049 participants detected 127 cases of oral cancer and precancerous lesions. Eight factors that are significantly associated with having a suspicious oral mucosal lesion at screening include tobacco smoking and snuff use, alcohol drinking, lack of fruits/vegetables consumption, and red/processed meat consumption, low spice consumption level, and comorbidities (p-value: <0.001-0.046). The predictive model based on the significant factors has an AUC of 0.74 (0.72 - 0.76) and Youden's index of 0.27 (0.25-0.29) that is higher than the metrics obtained for the conventional method of risk profiling for oral cancer (Youden's index: 0.25 (0.23-0.27)).
conclusionsRisk prediction model has better discrimination and net benefit than the conventional approach for identifying at-risk individuals for oral cancer. This finding supports the potential application of this method for risk stratification during targeted oral cancer screening.
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