Significant risk factors for oral mucosa cancer in the Altai Krai population

Relevance. Squamous cell carcinoma of the head and neck, including oral and oropharyngeal cancer, ranks as the seventh most common cancer worldwide, contributing to over 660,000 new cases and 325,000 deaths annually. Understanding the interplay of adverse factors, their association with disease development, and the creation of mathematical risk prediction models can play a crucial role in enhancing screening efforts and advancing primary prevention of malignant neoplasms.

Objective. This study aimed to identify significant risk factors for oral mucosa cancer in the Altai Krai population and to develop a mathematical model for disease risk assessment.

Material and methods. The study included 184 patients diagnosed with oral mucosa cancer, along with a control group of 416 healthy volunteers with no history or current diagnosis of oncological diseases. A total of 39 potential risk factors were analyzed across all participants. Statistical analyses were conducted to identify region-specific risk factors for Altai Krai. Binary logistic regression and ROC analysis were used to construct the risk prediction model.

Results. Comparative analysis between the patient group and the control group revealed differences in 19 of the 39 evaluated factors. However, the final risk prediction model identified five key factors significantly influencing disease development. Advanced age, smoking status, the number of cigarettes smoked, and alcohol consumption were found to substantially increase the risk of oral mucosa cancer, while engagement in intellectual work-related activities was associated with a reduced risk. The resulting formula demonstrated high predictive accuracy, with an Area Under the ROC Curve (AUC) of 0.91, a standard error of 0.024, and a 95% confidence interval of 0.856–0.955 (z-statistic: 17.50; significance level: P (area = 0.5) < 0.001). Both the sensitivity and specificity of the model were high.

Conclusion. The developed risk assessment model shows great promise for helping screen and diagnose oral cancer in the Altai Krai population. This tool could give dentists and healthcare providers a simple, practical way to identify individuals at risk early by using well-established risk factors.

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