| Title |
A Nomogram to Predict Brain Metastases of Resected Non-Small Cell Lung Cancer Patients
|
|---|---|
| Published in |
Annals of Surgical Oncology, April 2016
|
| DOI | 10.1245/s10434-016-5206-3 |
| Pubmed ID | |
| Authors |
Fanrong Zhang, Weihui Zheng, Lisha Ying, Junzhou Wu, Shaoyuan Wu, Shenglin Ma, Dan Su |
| Abstract |
Brain metastasis is a major cause leading to the failure of treatment management for non-small cell lung cancer (NSCLC) patients. The goal of this study was to establish an effective nomogram for prediction of brain metastases of resected NSCLC patients. We retrospectively investigated 637 operable NSCLC patients who received treatment at Zhejiang Cancer Hospital, China. A Cox proportional hazards regression model was performed to identify significant risk factors, and a nomogram was developed for predicting 3- and 5-year brain metastases rates. Multivariate analysis identified four independent risk factors: neuron-specific enolase, histological type, number of metastatic lymph nodes, and tumor grade, and a nomogram was developed based on these factors. The effectiveness of the nomogram was validated using an internal bootstrap resampling approach, showing that the nomogram exhibited a sufficient level of discrimination according to the C-index (0.74, 95 % confidence interval 0.67-0.82). The nomogram developed in this study demonstrated its discrimination capability for predicting 3- and 5-year occurrence of brain metastases, and can be used to identify high-risk patients. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 19% |
| Researcher | 4 | 15% |
| Student > Bachelor | 2 | 8% |
| Student > Doctoral Student | 2 | 8% |
| Student > Master | 2 | 8% |
| Other | 4 | 15% |
| Unknown | 7 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 35% |
| Computer Science | 3 | 12% |
| Agricultural and Biological Sciences | 1 | 4% |
| Mathematics | 1 | 4% |
| Social Sciences | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 8 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 07 August 2016.
All research outputs
#14,847,187
of 22,865,319 outputs
Outputs from Annals of Surgical Oncology
#4,258
of 6,482 outputs
Outputs of similar age
#170,036
of 299,111 outputs
Outputs of similar age from Annals of Surgical Oncology
#66
of 102 outputs
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We're also able to compare this research output to 102 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.