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Updated: Jun 30, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of nomogram models for predicting postoperative prognosis of early-stage laryngeal
Xu Juan1, Huang Jiali2, Liu Ziqi2
1Department of Otorhinolaryngology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, China; Department of Otorhinolaryngology-Head and Neck surgery, Shanghai Pudong New Area People's Hospital, Shanghai, China.
Background:
We aimed to investigate the postoperative prognosis in patients with early-stage laryngeal squamous cell carcinoma (LSCC) in association with the preoperative blood markers and clinicopathological characteristics and to develop nomograms for individual risk prediction.
Methods:
The clinical data of 353 patients with confirmed early-stage LSCC between 2009 and 2018 were retrospectively retrieved from the First Affiliated Hospital with Nanjing Medical University. All patients were randomly divided into the training and testing groups in a 7:3 ratio. Univariate and multivariate analyses were performed, followed by the construction of nomograms to predict recurrence-free survival (RFS) and overall survival (OS). Finally, the nomograms were verified internally, and the predictive capability of the nomograms was evaluated and compared with that of tumour T staging.
Results:
Univariate and multivariate analyses identified platelet counts (PLT), fibrinogen (FIB), and platelet to lymphocyte ratio (PLR) were independent factors for RFS, and FIB, systemic immune-inflammation index (SII), and haemoglobin (HGB) were independent prognostic factors for OS. The nomograms showed higher predictive C-indexes than T staging. Furthermore, decision curve analysis (DCA) revealed that the net benefit of the nomograms' calculation model was superior to that of T staging.
Conclusions:
We established and validated nomograms to predict postoperative 1-, 3- and 5-year RFS and OS in patients with early-stage LSCC based on significant blood markers and clinicopathological characteristics. These models might help clinicians make personalized treatment decisions.
