Conditional survival analysis May enhance prognosis estimate in buccal mucosa carcinoma

Background Buccal mucosa cancer (BMC) is an aggressive subtype of oral cancer. Conventional survival analysis cannot reflect the time-dependent nature of prognosis. This study aimed to assess dynamic survival probabilities in BMC using conditional survival (CS) analysis and to develop a CS-based nomogram for individualized prognostic evaluation. Methods Data of BMC patients diagnosed between 2004 and 2021 were obtained from the SEER database. CS analysis was applied to estimate dynamic survival, while annual hazard rate (AHR) analysis identified high-risk periods after diagnosis. A CS-nomogram was constructed using stepwise selection integrating best subsets regression, LASSO, and Cox analyses. Model performance was evaluated using calibration plots, area under the curve (AUC), decision curve analysis (DCA), and Kaplan–Meier stratification. Results A total of 2,310 patients were included. The 3-, 5-, and 10-year overall survival (OS) rates were 63%, 56%, and 41%, respectively. CS analysis showed a marked increase in 10-year survival probability, from 52% for 1-year survivors to 95% for 9-year survivors. AHR analysis revealed the highest mortality risk within the first year, which stabilized after three years. The CS-nomogram, incorporating key prognostic factors, achieved AUCs exceeding 0.80 in both training and validation cohorts. Conclusions This study establishes a dynamic prognostic framework for BMC. The CS-nomogram provides accurate, interval-updated survival predictions that can help clinicians tailor follow-up and counseling for long-term survivors. However, additional external validation in geographically diverse cohorts is needed.