Abstract
ObjectiveTo develop and validate a simple-to-use prognostic scoring model based on clinical and pathological features which can predict overall survival (OS) of patients with oral squamous cell carcinoma (OSCC) and facilitate personalized treatment planning.Materials and MethodsOSCC patients (n = 404) from a public hospital were divided into a training cohort (n = 282) and an internal validation cohort (n = 122). A total of 12 clinical and pathological features were included in Kaplan–Meier analysis to identify the factors associated with OS. Multivariable Cox proportional hazards regression analysis was performed to further identify important variables and establish prognostic models. Nomogram was generated to predict the individual’s 1-, 3- and 5-year OS rates. The performance of the prognostic scoring model was compared with that of the pathological one and the AJCC TNM staging system by the receiver operating characteristic curve (ROC), concordance index (C-index), calibration curve, and decision curve analysis (DCA). Patients were classified into high- and low-risk groups according to the risk scores of the nomogram. The nomogram-illustrated model was independently tested in an external validation cohort of 95 patients.ResultsFour significant variables (physical examination-tumor size, imaging examination-tumor size, pathological nodal involvement stage, and histologic grade) were included into the nomogram-illustrated model (clinical–pathological model). The area under the ROC curve (AUC) of the clinical–pathological model was 0.687, 0.719, and 0.722 for 1-, 3- and 5-year survival, respectively, which was superior to that of the pathological model (AUC = 0.649, 0.707, 0.717, respectively) and AJCC TNM staging system (AUC = 0.628, 0.668, 0.677, respectively). The clinical–pathological model exhibited improved discriminative power compared with pathological model and AJCC TNM staging system (C-index = 0.755, 0.702, 0.642, respectively) in the external validation cohort. The calibration curves and DCA also displayed excellent predictive performances.ConclusionThis clinical and pathological feature based prognostic scoring model showed better predictive ability compared with the pathological one, which would be a useful tool of personalized accurate risk stratification and precision therapy planning for OSCC patients.
Highlights
Prognostic prediction models are widely utilized both in clinic and research to estimate the probability that a certain outcome will occur within a specific time period in an individual [1]
We aimed to develop a prognostic scoring model using the widely available physical and imaging data, as well as the pathological data to predict 1, 3- and 5-year overall survival (OS) in Oral squamous cell carcinoma (OSCC) patients after surgery
Kaplan–Meier analysis showed that smoking history, primary site, physical examinationtumor size (PE-T), imaging examination-tumor size (IE-T), physical examination-nodal involvement (PE-N), IE-N, pathological tumor stage (P-T), pathological nodal involvement stage (P-N), and histologic grade were significantly associated with OS, while gender, age, and radiotherapy history displayed non-significance (p > 0.05) (Figure 2)
Summary
Prognostic prediction models are widely utilized both in clinic and research to estimate the probability that a certain outcome will occur within a specific time period in an individual [1]. A reliable prognostic model is essential in individual risk quantification and stratification, which is fundamental in personalized treatment plan development. Oral squamous cell carcinoma (OSCC) is the most common oral cancer, accounting for more than 90% of all oral cancers [4]. Advances in treatments improved the quality of life and life expectancy of patients. The 5-year overall survival rate of OSCC patients was still less than 60% [5]. How to assess the prognostic risk and choose the most suitable treatment for individuals is challenging for clinicians [6]
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