A novel nomogram and risk classification system for estimating survival in tracheal cancer patients treated with surgical intervention

Abstract

Abstract Purpose Although surgery has been considered as the first choice treatment for patients diagnosed with tracheal cancer, the prediction of overall survival (OS) for those who undergo surgical intervention is poor. To address this issue, we developed a nomogram that combines a risk classification system to estimate OS of tracheal cancer patients who have undergone surgical intervention. Methods The 525 qualified patients were identified from the SEER database between 1975 and 2018 and randomly divided into training cohort(TC) and validation cohort(VC). The parameters were detemined by Cox regression analyses for predicting OS and to form nomogram and risk classification. The nomogram predictive ability was tested by calibration curves and ROC curves. Kaplan-Meier curves were drawn and compared using the log-rank test for different risk classification groups. Results The results indicated that age, stage, histology, and tumor size were independent prognostic factors (PFs) and were included in the predictive model. The calibration plots demonstrated that the 24- and 36-month OS rates generated by the nomograms were in good agreement with the actual observed value of the 24- and 36-month OS rates. The ROC curves suggested that the predictive model had good discrimination ability. Furthermore, in the total cohort, TC and VC, the low-risk group had a better prognosis than the high-risk group. Conclusions A novel nomogram system to predict OS and identify independent PFs for tracheal cancer patients who have undergone surgical intervention was established in this study. This model performance has the potential to assist doctors in making decisions about treatment options.