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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring
|
|---|---|
| Published in |
Diagnostic Pathology, June 2012
|
| DOI | 10.1186/1746-1596-7-42 |
| Pubmed ID | |
| Authors |
Anthony E Rizzardi, Arthur T Johnson, Rachel Isaksson Vogel, Stefan E Pambuccian, Jonathan Henriksen, Amy PN Skubitz, Gregory J Metzger, Stephen C Schmechel |
| Abstract |
Immunohistochemical (IHC) assays performed on formalin-fixed paraffin-embedded (FFPE) tissue sections traditionally have been semi-quantified by pathologist visual scoring of staining. IHC is useful for validating biomarkers discovered through genomics methods as large clinical repositories of FFPE specimens support the construction of tissue microarrays (TMAs) for high throughput studies. Due to the ubiquitous availability of IHC techniques in clinical laboratories, validated IHC biomarkers may be translated readily into clinical use. However, the method of pathologist semi-quantification is costly, inherently subjective, and produces ordinal rather than continuous variable data. Computer-aided analysis of digitized whole slide images may overcome these limitations. Using TMAs representing 215 ovarian serous carcinoma specimens stained for S100A1, we assessed the degree to which data obtained using computer-aided methods correlated with data obtained by pathologist visual scoring. To evaluate computer-aided image classification, IHC staining within pathologist annotated and software-classified areas of carcinoma were compared for each case. Two metrics for IHC staining were used: the percentage of carcinoma with S100A1 staining (%Pos), and the product of the staining intensity (optical density [OD] of staining) multiplied by the percentage of carcinoma with S100A1 staining (OD*%Pos). A comparison of the IHC staining data obtained from manual annotations and software-derived annotations showed strong agreement, indicating that software efficiently classifies carcinomatous areas within IHC slide images. Comparisons of IHC intensity data derived using pixel analysis software versus pathologist visual scoring demonstrated high Spearman correlations of 0.88 for %Pos (p < 0.0001) and 0.90 for OD*%Pos (p < 0.0001). This study demonstrated that computer-aided methods to classify image areas of interest (e.g., carcinomatous areas of tissue specimens) and quantify IHC staining intensity within those areas can produce highly similar data to visual evaluation by a pathologist. |
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 % |
|---|---|---|
| Brazil | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 469 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 1% |
| United Kingdom | 3 | <1% |
| Colombia | 1 | <1% |
| France | 1 | <1% |
| Chile | 1 | <1% |
| Germany | 1 | <1% |
| Singapore | 1 | <1% |
| Canada | 1 | <1% |
| Spain | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 453 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 81 | 17% |
| Student > Bachelor | 70 | 15% |
| Student > Master | 64 | 14% |
| Researcher | 50 | 11% |
| Student > Postgraduate | 42 | 9% |
| Other | 88 | 19% |
| Unknown | 74 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 136 | 29% |
| Agricultural and Biological Sciences | 64 | 14% |
| Biochemistry, Genetics and Molecular Biology | 62 | 13% |
| Engineering | 37 | 8% |
| Computer Science | 15 | 3% |
| Other | 56 | 12% |
| Unknown | 99 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 05 December 2019.
All research outputs
#6,378,944
of 22,664,644 outputs
Outputs from Diagnostic Pathology
#164
of 1,118 outputs
Outputs of similar age
#44,792
of 163,862 outputs
Outputs of similar age from Diagnostic Pathology
#2
of 16 outputs
Altmetric has tracked 22,664,644 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,118 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done well, scoring higher than 84% 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 163,862 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.