| Title |
Nomogram for predicting survival in patients with pancreatic cancer
|
|---|---|
| Published in |
OncoTargets and therapy, January 2018
|
| DOI | 10.2147/ott.s154599 |
| Pubmed ID | |
| Authors |
Wei Song, Dong-Liu Miao, Lei Chen |
| Abstract |
The purpose of this study was to develop a nomogram to predict cancer-specific survival (CSS) in pancreatic cancer (PC). We used the Surveillance, Epidemiology, and End Results (SEER) database to analyze 53,028 patients diagnosed with PC from 2004 to 2014 and randomly divided them into the training (n=26,583) cohort and validation (n=26,445) cohort. Univariate and multivariate analyses were used to select independent prognostic factors. We used significant prognostic factors for constructing a nomogram based on Cox regression analyses. Validation of the nomogram was assessed by discrimination and calibration. According to the multivariate models of training cohort, a nomogram that combined age, race, tumor location, marital status, tumor size, TNM stage, tumor grade, and surgery was constructed for predicting CSS. The internally validated and externally validated C-indexes were 0.741 and 0.734, respectively. The calibration curves showed that the nomogram was able to predict 1-, 3-, and 5-year CSS accurately. A nomogram effectively predicts survival in patients with PC. This prognostic model may be considered for use in clinical practice. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 42 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 19% |
| Student > Doctoral Student | 7 | 17% |
| Student > Bachelor | 6 | 14% |
| Student > Ph. D. Student | 5 | 12% |
| Student > Postgraduate | 4 | 10% |
| Other | 7 | 17% |
| Unknown | 5 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 18 | 43% |
| Biochemistry, Genetics and Molecular Biology | 5 | 12% |
| Agricultural and Biological Sciences | 3 | 7% |
| Computer Science | 2 | 5% |
| Engineering | 2 | 5% |
| Other | 5 | 12% |
| Unknown | 7 | 17% |
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 09 February 2018.
All research outputs
#19,951,180
of 25,382,440 outputs
Outputs from OncoTargets and therapy
#1,447
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Outputs of similar age
#325,409
of 449,550 outputs
Outputs of similar age from OncoTargets and therapy
#33
of 69 outputs
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