Nomograms for predicting survival outcomes in patients with primary tracheal tumors: a large population-based analysis
Received 5 September 2018
Accepted for publication 16 November 2018
Published 11 December 2018 Volume 2018:10 Pages 6843—6856
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Andrew Yee
Peer reviewer comments 2
Editor who approved publication: Dr Rituraj Purohit
Junmiao Wen,1,2,* Di Liu,1,2,* Xinyan Xu,1,2 Donglai Chen,3 Yongbing Chen,4 Liang Sun,5 Jiayan Chen,1,2 Min Fan1,2
1Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People’s Republic of China; 2Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People’s Republic of China; 3Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, People’s Republic of China; 4Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou 215000, People’s Republic of China; 5School of Radiation Medicine and Protection, Medical College of Soochow University, Suzhou 215123, People’s Republic of China
*These authors contributed equally to this work
Background: The aim of this study was to develop and validate reliable nomograms to predict individual overall survival (OS) and cancer-specific survival (CSS) for patients with primary tracheal tumors and further estimate the role of postoperative radiotherapy (PORT) for these entities.
Patients and methods: A total of 405 eligible patients diagnosed between 1988 and 2015 were selected from the Surveillance, Epidemiology, and End Results database. All of them were randomly divided into training (n=303) and validation (n=102) sets. For the purpose of establishing nomograms, the Akaike information criterion was employed to select significant prognostic factors in multivariate Cox regression models. Both internal and external validations of the nomograms were evaluated by Harrell’s concordance index (C-index) and calibration plots. Propensity score matching (PSM) method was performed to reduce the influence of selection bias between the PORT group and the non-PORT group.
Results: Two nomograms shared common variables including age at diagnosis, histology, N and M stages, tumor size, and treatment types, while gender was only incorporated in the CSS nomogram. The C-indices of OS and CSS nomograms were 0.817 and 0.813, displaying considerable predictive accuracy. The calibration curves indicated consistency between the nomograms and the actual observations. When the nomograms were applied to the validation set, the results remained reconcilable. Moreover, the nomograms showed superiority over the Bhattacharyya’s staging system with regard to the C-indices. After PSM, PORT was not associated with significantly better OS or CSS. Only squamous cell carcinoma (SCC) patients in the PORT group had improved OS compared to non-PORT group.
Conclusion: The first two nomograms for predicting survival in patients with primary tracheal tumors were proposed in the present study. PORT seems to improve the prognosis of SCC patients, which needs further exploration.
Keywords: nomogram, primary tracheal tumors, overall survival, cancer-specific survival, propensity score matching analysis
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