Cancer Medicine (Jul 2019)

Nomograms for predicting long‐term overall survival and cancer‐specific survival in lip squamous cell carcinoma: A population‐based study

  • Chuan-Yu Hu,
  • Zhen-Yu Pan,
  • Jin Yang,
  • Xiu-Hong Chu,
  • Jun Zhang,
  • Xue-Jin Tao,
  • Wei-Min Chen,
  • Yuan-Jie Li,
  • Jun Lyu

DOI
https://doi.org/10.1002/cam4.2260
Journal volume & issue
Vol. 8, no. 8
pp. 4032 – 4042

Abstract

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Abstract Background The goal of this study was to establish and validate two nomograms for predicting the long‐term overall survival (OS) and cancer‐specific survival (CSS) in lip squamous cell carcinoma (LSCC). Methods This study selected 4175 patients who were diagnosed with LSCC between 2004 and 2015 in the SEER (Surveillance, Epidemiology, and End Results) database. The patients were allocated randomly to a training cohort and validation cohort. Variables were selected using a backward stepwise method in a Cox regression model. Based on the predictive model with the identified prognostic factors, nomograms were established to predict the 3‐, 5‐, and 8‐year survival OS and CSS rates of LSCC patients. The accuracy of the nomograms was evaluated based on the consistency index (C‐index), while their prediction accuracy was evaluated using calibration plots. Decision curve analyses (DCAs) were used to evaluate the performance of our survival model. Results The multivariate analyses demonstrated that age at diagnosis, marital status, sex, race, American Joint Committee on Cancer stage, surgery status, and radiotherapy status were risk factors for both OS and CSS. The C‐index, area under the time‐dependent receiver operating characteristic curve, and calibration plots demonstrated the good performance of the nomograms. DCAs of both nomograms further showed that they exhibited good 3‐, 5‐, and 8‐year net benefits. Conclusions We have developed and validated LSCC prognosis nomograms for OS and CSS for the first time. These nomograms can be valuable tools for clinical practice when clinicians are helping patients to understand their survival risk for the next 3, 5, and 8 years.

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