Nomograms for predicting prognosis in postoperative lip squamous cell carcinoma patients

Abstract

Abstract Background Lip squamous cell carcinoma (LSCC) is the most commonly occurring oral cavity cancer. This study aimed to establish and validate comprehensive nomograms for predicting the prognosis in postoperative LSCC patients. Methods A total of 136 postoperative lip squamous cell carcinoma (LSCC) patients diagnosed between June 2012 and June 2018 were enrolled from two medical institutions and randomly divided into the training and validation groups at a ratio of 7:3. According to the results of the univariate and multivariate Cox regression analyses, six independent indicators concerning overall survival (OS) were identified, including age, grade, T-stage, lymph node metastasis (LNM), perineural invasion (PNI), vascular invasion (VI), surgical margin. Besides, age, grade, T-stage, lymph node metastasis (LNM), perineural invasion (PNI), and surgical margin were independent predictors of disease-free survival (DFS) in LSCC patients. The two nomograms for predicting OS and DFS were developed based on the above results. Results The univariate and multivariate Cox regression analysis showed that higher pathological grade, age ≥ 70 years, higher T-stage, positive lymph node metastasis (LNM), perineural invasion (PNI), vascular invasion (VI), and positive surgical margin were independent predictors of inferior OS. Meanwhile, higher pathological grade, age ≥ 70 years, higher T-stage, LNM, PNI, and positive surgical margin were independent predictors of inferior DFS. Based on the results above, two nomograms were constructed to predict 3- and 5-year OS and DFS in patients with LSCC. The C-indexes of the OS and DFS nomograms were 0.865 and 0.801 in the training group, and 0.915 and 0.815 in the validation group. The calibration curves showed satisfactory consistency between predicted and actual observed survival rates. The outperformance of the nomogram compared with the other predictors involved was shown by the decision curve analysis (DCA). Kaplan-Meier curves revealed statistical discrimination for the high-, medium-, and low-risk groups. Conclusion Two nomograms for predicting OS and DFS in patients with postoperative LSCC developed in this study perform well, which may be helpful for oncologists and surgeons to choose proper individual therapeutic schedules and design appropriate follow-up strategies.