Comprehensive Summary
The article investigates the factors contributing to recurrence of head and neck squamous cell carcinoma (HNSCC) to improve risk stratification. To identify the risk factors for rapid recurrence (RR), the study integrates additional recurrence time intervals – short-interval recurrence (SIR) and standard recurrence (SR) – in a retrospective 10-year review of HNSCC patients who underwent surgery followed by adjuvant therapy. Both univariate analysis (UVA) and multivariate analysis (MVA) were performed on the three recurrence types to predict the recurrence type, while machine learning Random Forest models were conducted to identify predictors of each variable. Among 246 patients, 89 experience recurrence. The MVA revealed that skin invasion was a unique predictor of RR, and features such as package time, tobacco pack-years, and percent positive lymph nodal burden influenced SIR and SR. These findings highlight the association between skin invasion and RR with the use of statistical analysis methods.
Outcomes and Implications
The study concludes that tumoral skin invasion is an independent predictor of RR. This finding suggests that patients with skin invasion may benefit from earlier intervention with alternative treatment strategies and continued advancements in immuno-oncologic approaches. Although the retrospective cohort study has limitations – small sample sizes, unbalanced distribution of patients, limited generalizability – the study clearly links skin invasion with RR, underscoring the necessity for future research on the effect of alternative treatment strategies on patient survival. Moreover, the use of MVA and Random Forest machine learning models enhances the study’s findings and highlights potential implementation of statistical analysis models in clinical trials, treatment planning, and neoadjuvant therapy development.