Prognostic Disparities in Multiple versus Single Primary OSCC: A Large-Cohort Analysis and Predictive Modelling

The rising incidence of multiple primary cancers among patients with oral squamous cell carcinoma (OSCC-MPCs) presents a profound clinical challenge. This study aimed to characterize the clinicopathological phenotypes and survival outcomes of OSCC-MPCs compared with those of single primary OSCC (SPOSCC), and to develop robust prognostic nomograms for the OSCC-MPCs population.

In this single-centre retrospective cohort study (2015-2025), clinicopathological and systemic inflammatory indices were extracted. Survival outcomes were compared using Kaplan-Meier analysis. Least Absolute Shrinkage and Selection Operator (LASSO) coupled with multivariable Cox regression were employed to identify independent predictors and construct nomograms for overall survival (OS) and progression-free survival (PFS).

Among the 1,909 patients (OSCC-MPCs: n = 249; SPOSCC: n = 1,660), the OSCC-MPCs cohort exhibited an older age at diagnosis, lower rates of smoking and alcohol consumption, higher T but lower N categories, and significantly elevated preoperative neutrophil-to-lymphocyte ratios (NLR). Survival analysis based on complete survival data (OSCC-MPCs: n = 249; SPOSCC: n = 1,546) revealed significantly inferior outcomes in the OSCC-MPCs group compared with the SPOSCC group, demonstrating markedly reduced 5-year OS (51.28% vs. 79.85%, P < .001) and PFS (37.97% vs. 63.05%, P < .001) rates. LASSO-Cox regression identified age > 60 gt; 60 years, advanced T/N categories, NLR > 2.5, and specific treatment modalities as independent prognostic factors. The resulting dual-endpoint nomograms demonstrated reliable discrimination, optimal calibration, and substantial clinical net benefit.

OSCC-MPCs exhibit highly distinct clinical phenotypes and are associated with significantly poorer survival compared with SPOSCC. Our nomograms, integrating clinicopathological features with NLR, provide robust, non-invasive quantitative tools to tailor individualized therapeutic interventions and optimize long-term surveillance strategies.