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

Overview of attention for article published in BMC Cancer, October 2016
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Title
Prognostic value of computed tomography characteristics for overall survival in patients with maxillary cancer
Published in
BMC Cancer, October 2016
DOI 10.1186/s12885-016-2830-z
Pubmed ID
Authors

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

Abstract

Our aim was to identify the preoperative computed tomographic (CT) characteristics most efficient in predicting overall survival (OS) of patients with maxillary cancer (MC). 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. 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). 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.

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The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 2 14%
Lecturer > Senior Lecturer 1 7%
Librarian 1 7%
Student > Bachelor 1 7%
Student > Ph. D. Student 1 7%
Other 2 14%
Unknown 6 43%
Readers by discipline Count As %
Nursing and Health Professions 2 14%
Medicine and Dentistry 2 14%
Engineering 1 7%
Unknown 9 64%