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Prognostic models and nomograms for predicting survival of patients with maxillary sinus carcinomas
Author(s) -
Shen Weidong,
Sakamoto Naoko,
Yang Limin
Publication year - 2017
Publication title -
international forum of allergy and rhinology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.503
H-Index - 46
eISSN - 2042-6984
pISSN - 2042-6976
DOI - 10.1002/alr.21950
Subject(s) - medicine , nomogram , proportional hazards model , stage (stratigraphy) , maxillary sinus , hazard ratio , epidemiology , confidence interval , malignancy , survival analysis , prognostic variable , oncology , censoring (clinical trials) , surgery , radiology , cancer , pathology , paleontology , biology
Background Maxillary sinus carcinoma is an uncommon malignancy. Most reports on prognosis of this disease are from single institutions and include few patients. We used data from the United States National Cancer Institute's Surveillance Epidemiology and End Results (SEER) program to construct models and nomograms for predicting outcomes of patients with maxillary sinus carcinomas. Methods We used records from 668 patients with primary maxillary sinus carcinomas reported to the SEER program from 2004 to 2013 to build nomograms based on stratified multivariable Cox proportional hazard models for predicting 5‐year overall survival (OS) and cause‐specific survival (CSS). Model building was internally validated with the bootstrap approach. Results Five‐year survival was 39.7% (95% confidence interval [CI], 35.5% to 44.5%) and 46.8% (42.3% to 51.8%) for OS and CSS, respectively. The final Cox model included the variables of age at diagnosis, tumor size, histologic type, TNM stage, and surgery. Radiotherapy was a stratification factor in the models. The models demonstrated good accuracy for predicting survival with a bootstrap‐corrected Somers D xy of 0.44 for both OS and CSS models. Calibration curves indicated acceptable model calibration. Conclusion We developed tools for predicting prognosis that incorporate TNM stage and other readily available variables for patients with maxillary sinus carcinomas. The model performance was validated as good. These models can help clinicians to offer improved patient counseling in terms of clinical outcomes and make optimal treatment plans.