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Mendeley readers
Attention Score in Context
| Title |
Automated Selection of Hotspots (ASH): enhanced automated segmentation and adaptive step finding for Ki67 hotspot detection in adrenal cortical cancer
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|---|---|
| Published in |
Diagnostic Pathology, November 2014
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| DOI | 10.1186/s13000-014-0216-6 |
| Pubmed ID | |
| Authors |
Hao Lu, Thomas G Papathomas, David van Zessen, Ivo Palli, Ronald R de Krijger, Peter J van der Spek, Winand NM Dinjens, Andrew P Stubbs |
| Abstract |
BackgroundIn prognosis and therapeutics of adrenal cortical carcinoma (ACC), the selection of the most active areas in proliferative rate (hotspots) within a slide and objective quantification of immunohistochemical Ki67 Labelling Index (LI) are of critical importance. In addition to intratumoral heterogeneity in proliferative rate i.e. levels of Ki67 expression within a given ACC, lack of uniformity and reproducibility in the method of quantification of Ki67 LI may confound an accurate assessment of Ki67 LI.ResultsWe have implemented an open source toolset, Automated Selection of Hotspots (ASH), for automated hotspot detection and quantification of Ki67 LI. ASH utilizes NanoZoomer Digital Pathology Image (NDPI) splitter to convert the specific NDPI format digital slide scanned from the Hamamatsu instrument into a conventional tiff or jpeg format image for automated segmentation and adaptive step finding hotspots detection algorithm. Quantitative hotspot ranking is provided by the functionality from the open source application ImmunoRatio as part of the ASH protocol. The output is a ranked set of hotspots with concomitant quantitative values based on whole slide ranking.ConclusionWe have implemented an open source automated detection quantitative ranking of hotspots to support histopathologists in selecting the `hottest¿ hotspot areas in adrenocortical carcinoma. To provide wider community easy access to ASH we implemented a Galaxy virtual machine (VM) of ASH which is available from http://bioinformatics.erasmusmc.nl/wiki/index.php/Automated Selection of Hotspots.Virtual SlidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_216. |
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 % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 3% |
| South Africa | 1 | 3% |
| Unknown | 32 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 32% |
| Researcher | 5 | 15% |
| Student > Master | 5 | 15% |
| Professor > Associate Professor | 3 | 9% |
| Student > Bachelor | 3 | 9% |
| Other | 4 | 12% |
| Unknown | 3 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 8 | 24% |
| Engineering | 7 | 21% |
| Computer Science | 6 | 18% |
| Agricultural and Biological Sciences | 5 | 15% |
| Chemistry | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 6 | 18% |
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 04 December 2014.
All research outputs
#13,416,718
of 22,771,140 outputs
Outputs from Diagnostic Pathology
#348
of 1,123 outputs
Outputs of similar age
#178,250
of 361,642 outputs
Outputs of similar age from Diagnostic Pathology
#14
of 54 outputs
Altmetric has tracked 22,771,140 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,123 research outputs from this source. They receive a mean Attention Score of 2.8. This one has gotten more attention than average, scoring higher than 65% of its peers.
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 361,642 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.