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Multicenter Study
. 2015 Mar 10;33(8):861-9.
doi: 10.1200/JCO.2014.56.6661. Epub 2015 Jan 26.

Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer

Affiliations
Multicenter Study

Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer

Wenhua Liang et al. J Clin Oncol. .

Abstract

Purpose: A nomogram is a useful and convenient tool for individualized cancer prognoses. We sought to develop a clinical nomogram for predicting survival of patients with resected non-small-cell lung cancer (NSCLC).

Patients and methods: On the basis of data from a multi-institutional registry of 6,111 patients with resected NSCLC in China, we identified and integrated significant prognostic factors for survival to build a nomogram. The model was subjected to bootstrap internal validation and to external validation with a separate cohort of 2,148 patients from the International Association for the Study of Lung Cancer (IASLC) database. The predictive accuracy and discriminative ability were measured by concordance index (C-index) and risk group stratification.

Results: A total of 5,261 patients were included for analysis. Six independent prognostic factors were identified and entered into the nomogram. The calibration curves for probability of 1-, 3-, and 5-year overall survival (OS) showed optimal agreement between nomogram prediction and actual observation. The C-index of the nomogram was higher than that of the seventh edition American Joint Committee on Cancer TNM staging system for predicting OS (primary cohort, 0.71 v 0.68, respectively; P < .01; IASLC cohort, 0.67 v 0.64, respectively; P = .06). The stratification into different risk groups allowed significant distinction between survival curves within respective TNM categories.

Conclusion: We established and validated a novel nomogram that can provide individual prediction of OS for patients with resected NSCLC. This practical prognostic model may help clinicians in decision making and design of clinical studies.

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