You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computationally-Guided Development of a Stromal Inflammation Histologic Biomarker in Lung Squamous Cell Carcinoma
|
|---|---|
| Published in |
Scientific Reports, March 2018
|
| DOI | 10.1038/s41598-018-22254-4 |
| Pubmed ID | |
| Authors |
Daniel Xia, Ruben Casanova, Devayani Machiraju, Trevor D. McKee, Walter Weder, Andrew H. Beck, Alex Soltermann |
| Abstract |
The goal of this study is to use computational pathology to help guide the development of human-based prognostic H&E biomarker(s) suitable for research and potential clinical use in lung squamous cell carcinoma (SCC). We started with high-throughput computational image analysis with tissue microarrays (TMAs) to screen for histologic features associated with patient overall survival, and found that features related to stromal inflammation were the most strongly prognostic. Based on this, we developed an H&E stromal inflammation (SI) score. The prognostic value of the SI score was validated by two blinded human observers on two large cohorts from a single institution. The SI score was found to be reproducible on TMAs (Spearman rho = 0.88 between the two observers), and highly prognostic (e.g. hazard ratio = 0.32; 95% confidence interval: 0.19-0.54; p-value = 2.5 × 10-5in multivariate analyses), particularly in comparison to established histologic biomarkers. Guided by downstream molecular/biomarker correlation studies starting with TCGA cases, we investigated the hypothesis that epithelial PD-L1 expression modified the prognostic value of SI. Our research demonstrates that computational pathology can be an efficient hypothesis generator for human pathology research, and support the histologic evaluation of SI as a prognostic biomarker in lung SCCs. |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 16% |
| Researcher | 4 | 16% |
| Student > Bachelor | 3 | 12% |
| Student > Doctoral Student | 2 | 8% |
| Student > Postgraduate | 2 | 8% |
| Other | 6 | 24% |
| Unknown | 4 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 20% |
| Computer Science | 5 | 20% |
| Agricultural and Biological Sciences | 3 | 12% |
| Neuroscience | 2 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 6 | 24% |
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 20 March 2018.
All research outputs
#14,094,948
of 23,026,672 outputs
Outputs from Scientific Reports
#65,170
of 124,375 outputs
Outputs of similar age
#181,866
of 331,406 outputs
Outputs of similar age from Scientific Reports
#2,098
of 3,947 outputs
Altmetric has tracked 23,026,672 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 124,375 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,406 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3,947 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.