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Cancer classification using the Immunoscore: a worldwide task force

Overview of attention for article published in Journal of Translational Medicine, October 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
8 X users
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5 patents
wikipedia
4 Wikipedia pages

Citations

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675 Dimensions

Readers on

mendeley
666 Mendeley
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1 CiteULike
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Title
Cancer classification using the Immunoscore: a worldwide task force
Published in
Journal of Translational Medicine, October 2012
DOI 10.1186/1479-5876-10-205
Pubmed ID
Authors

Jérôme Galon, Franck Pagès, Francesco M Marincola, Helen K Angell, Magdalena Thurin, Alessandro Lugli, Inti Zlobec, Anne Berger, Carlo Bifulco, Gerardo Botti, Fabiana Tatangelo, Cedrik M Britten, Sebastian Kreiter, Lotfi Chouchane, Paolo Delrio, Hartmann Arndt, Martin Asslaber, Michele Maio, Giuseppe V Masucci, Martin Mihm, Fernando Vidal-Vanaclocha, James P Allison, Sacha Gnjatic, Leif Hakansson, Christoph Huber, Harpreet Singh-Jasuja, Christian Ottensmeier, Heinz Zwierzina, Luigi Laghi, Fabio Grizzi, Pamela S Ohashi, Patricia A Shaw, Blaise A Clarke, Bradly G Wouters, Yutaka Kawakami, Shoichi Hazama, Kiyotaka Okuno, Ena Wang, Jill O'Donnell-Tormey, Christine Lagorce, Graham Pawelec, Michael I Nishimura, Robert Hawkins, Réjean Lapointe, Andreas Lundqvist, Samir N Khleif, Shuji Ogino, Peter Gibbs, Paul Waring, Noriyuki Sato, Toshihiko Torigoe, Kyogo Itoh, Prabhu S Patel, Shilin N Shukla, Richard Palmqvist, Iris D Nagtegaal, Yili Wang, Corrado D'Arrigo, Scott Kopetz, Frank A Sinicrope, Giorgio Trinchieri, Thomas F Gajewski, Paolo A Ascierto, Bernard A Fox

Abstract

Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J Transl Med. editorial from January 2012. Immunophenotyping of tumors may provide crucial novel prognostic information. The results of this international validation may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 666 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 <1%
Portugal 2 <1%
France 2 <1%
United Kingdom 2 <1%
Netherlands 1 <1%
Norway 1 <1%
Czechia 1 <1%
Germany 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 651 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 149 22%
Student > Ph. D. Student 112 17%
Student > Master 56 8%
Other 55 8%
Student > Bachelor 50 8%
Other 131 20%
Unknown 113 17%
Readers by discipline Count As %
Medicine and Dentistry 212 32%
Agricultural and Biological Sciences 101 15%
Biochemistry, Genetics and Molecular Biology 86 13%
Immunology and Microbiology 61 9%
Computer Science 13 2%
Other 52 8%
Unknown 141 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 24 January 2021.
All research outputs
#2,178,646
of 26,017,215 outputs
Outputs from Journal of Translational Medicine
#389
of 4,712 outputs
Outputs of similar age
#14,617
of 195,342 outputs
Outputs of similar age from Journal of Translational Medicine
#5
of 57 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,712 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has done particularly well, scoring higher than 91% 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 195,342 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.