Prognostic value of computed tomography characteristics for overall survival in patients with maxillary cancer

Ying Yuan, Jingbo Wang, Yingwei Wu, Guojun Li, Xiaofeng Tao

Research output: Contribution to journalArticle


Background: Our aim was to identify the preoperative computed tomographic (CT) characteristics most efficient in predicting overall survival (OS) of patients with maxillary cancer (MC). Methods: A retrospective review of CT images was performed in 115 patients with histopathologically confirmed primary MC from January 2005 to December 2013, who were classified into 2 subtypes (epithelial and non-epithelial) according to tissue of origin. The prognostic value of CT characteristics for OS was determined firstly through univariate Kaplan-Meier survival estimates with log-rank tests. Significant predictors were further tested with multivariable Cox proportional hazard models. Results: CT characteristics predictive of OS in univariate survival analysis were long and short diameter of the mass, long and short diameter of the largest cervical lymph node and adjacent soft tissue infiltration (P < 0.05). In the multivariable Cox analyses, the significantly independent predictors were long diameter of mass ≥ 4.2 cm (hazard ratio [HR] 1.8; 95 % confidence interval [CI] 1.1-3.0) and short diameter of the largest lymph node ≥ 7 mm (HR 1.9; 95 % CI 1.0-3.6) for all MC patients, as well as for non-epithelial MC patients (HR 3.1; 95 % CI 1.2-8.0; HR 3.3; 95 % CI 1.3-8.7, respectively). Conclusions: Preoperative CT characteristics of tumor size, lymph node size and adjacent structure infiltration are predictive of the OS time of MC patients. The information brought up in this study could be used in clinical practice to inform about the possible prognosis, and be beneficial to clinical decision making.

Original languageEnglish (US)
Article number783
JournalBMC cancer
Issue number1
StatePublished - Oct 10 2016


  • Cancer
  • Computed tomography
  • Maxilla
  • Overall survival

ASJC Scopus subject areas

  • Genetics
  • Oncology
  • Cancer Research

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