Abstract Oral cancer is a significant health issue in India, often diagnosed late, resulting in poor outcomes/prognosis. Early identification of high-risk individuals is crucial for preventing complications, and focusing on these populations can significantly improve screening efforts. Implementing a risk assessment tool for oral cancer may enhance examination strategies across the country. Our objective was to develop a risk assessment model for oral cavity cancer based on a comprehensive understanding of risk factors, to ensure generalizability across Indian populations. A multicenter case–control study was conducted from October 2022 to July 2023 across three cancer hospitals in Telangana, India to identify oral cancer risk factors. A risk score for each predictor was derived from the respective odds ratios (OR). The predictive ability of the regression model and the cut-off risk score were determined by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Calibration plots and the Hosmer–Lemeshow goodness-of-fit test were used to assess how well each model's predicted probabilities align with primary and secondary outcomes. Brier score was used as a measure of the model's overall accuracy. Decision curve analysis evaluated the model's clinical utility and net benefit for risk prediction. The models were validated using a bootstrap sample and OR from pooled studies from a systematic review. Years of smoked and smokeless tobacco, alcohol frequency, use of vegetables in the diet, and history of chronic oral trauma were the predictors. Risk scores ranged from −1 to 2. Area under the receiver operating characteristic curve for risk scores was good (0.76–0.840). Sensitivity was highest for upper socio-economic class, and pooled models while multivariable and bootstrapped upper socio-economic class models had the highest for specificity. Brier score of 0.1322 for the upper class and 0.1673 for the lower class indicated optimal model performance, while those for multivariable and pooled data models indicated suboptimal model performance. The risk scoring model showed the ability to identify individuals at high risk for oral cancer, demonstrating good predictive ability for the Indian population. It needs validation in other populations to accurately pinpoint subgroups needing further clinical evaluation.
Mocherla et al. (Sat,) studied this question.