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Establishment and validation of a predictive nomogram model for non-small cell lung cancer patients with chronic hepatitis B viral infection

Overview of attention for article published in Journal of Translational Medicine, May 2018
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Title
Establishment and validation of a predictive nomogram model for non-small cell lung cancer patients with chronic hepatitis B viral infection
Published in
Journal of Translational Medicine, May 2018
DOI 10.1186/s12967-018-1496-5
Pubmed ID
Authors

Shulin Chen, Yanzhen Lai, Zhengqiang He, Jianpei Li, Xia He, Rui Shen, Qiuying Ding, Hao Chen, Songguo Peng, Wanli Liu

Abstract

This study aimed to establish an effective predictive nomogram for non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection. The nomogram was based on a retrospective study of 230 NSCLC patients with chronic HBV infection. The predictive accuracy and discriminative ability of the nomogram were determined by a concordance index (C-index), calibration plot and decision curve analysis and were compared with the current tumor, node, and metastasis (TNM) staging system. Independent factors derived from Kaplan-Meier analysis of the primary cohort to predict overall survival (OS) were all assembled into a Cox proportional hazards regression model to build the nomogram model. The final model included age, tumor size, TNM stage, treatment, apolipoprotein A-I, apolipoprotein B, glutamyl transpeptidase and lactate dehydrogenase. The calibration curve for the probability of OS showed that the nomogram-based predictions were in good agreement with the actual observations. The C-index of the model for predicting OS had a superior discrimination power compared with the TNM staging system [0.780 (95% CI 0.733-0.827) vs. 0.693 (95% CI 0.640-0.746), P < 0.01], and the decision curve analyses showed that the nomogram model had a higher overall net benefit than did the TNM stage. Based on the total prognostic scores (TPS) of the nomogram, we further subdivided the study cohort into three groups: low risk (TPS ≤ 13.5), intermediate risk (13.5 < TPS ≤ 20.0) and high risk (TPS > 20.0). The proposed nomogram model resulted in more accurate prognostic prediction for NSCLC patients with chronic HBV infection.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 18%
Student > Ph. D. Student 1 9%
Student > Doctoral Student 1 9%
Other 1 9%
Student > Master 1 9%
Other 2 18%
Unknown 3 27%
Readers by discipline Count As %
Medicine and Dentistry 4 36%
Social Sciences 1 9%
Biochemistry, Genetics and Molecular Biology 1 9%
Pharmacology, Toxicology and Pharmaceutical Science 1 9%
Engineering 1 9%
Other 0 0%
Unknown 3 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 May 2018.
All research outputs
#11,463,516
of 12,896,510 outputs
Outputs from Journal of Translational Medicine
#2,410
of 2,548 outputs
Outputs of similar age
#233,565
of 269,440 outputs
Outputs of similar age from Journal of Translational Medicine
#1
of 1 outputs
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So far Altmetric has tracked 2,548 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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