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Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors

Overview of attention for article published in Cancer Research, April 2004
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
1 news outlet
patent
8 patents

Citations

dimensions_citation
124 Dimensions

Readers on

mendeley
40 Mendeley
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Title
Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors
Published in
Cancer Research, April 2004
DOI 10.1158/0008-5472.can-03-3811
Pubmed ID
Authors

Chiara Mazzanti, Martha A. Zeiger, Nick Costourous, Christopher Umbricht, William H Westra, Danelle Smith, Helina Somervell, Generoso Bevilacqua, H. Richard Alexander, Steven K. Libutti

Abstract

DNA microarrays allow quick and complete evaluation of a cell's transcriptional activity. Expression genomics is very powerful in that it can generate expression data for a large number of genes simultaneously across multiple samples. In cancer research, an intriguing application of expression arrays includes assessing the molecular components of the neoplastic process and utilizing the data for cancer classification (Miller LD, et al. Cancer Cell 2002;2:353-61). Classification of human cancers into distinct groups based on their molecular profile rather than their histological appearance may prove to be more relevant to specific cancer diagnoses and cancer treatment regimes. Several attempts to formulate a consensus about classification and treatment of thyroid carcinoma based on standard histopathological analysis have resulted in published guidelines for diagnosis and initial disease management (Sherman SI. Lancet 2003;361:501-11). In the past few decades, no improvement has been made in the differential diagnosis of thyroid tumors by fine needle aspiration biopsy, specifically suspicious or indeterminate thyroid lesions, suggesting that a new approach to this should be explored. Therefore, in this study, we developed a gene expression approach to diagnose benign versus malignant thyroid lesions in 73 patients with thyroid tumors. We successfully built a 10 and 6 gene model able to differentiate benign versus malignant thyroid tumors. Our results support the premise that a molecular classification system for thyroid tumors is possible, and this in turn may provide a more accurate diagnostic tool for the clinician managing patients with suspicious thyroid lesions.

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 8%
United Kingdom 2 5%
Unknown 35 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 15%
Professor > Associate Professor 6 15%
Other 5 13%
Student > Ph. D. Student 5 13%
Professor 3 8%
Other 9 23%
Unknown 6 15%
Readers by discipline Count As %
Medicine and Dentistry 17 43%
Agricultural and Biological Sciences 10 25%
Biochemistry, Genetics and Molecular Biology 4 10%
Computer Science 1 3%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 0 0%
Unknown 7 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 September 2022.
All research outputs
#1,557,328
of 22,113,391 outputs
Outputs from Cancer Research
#1,078
of 17,634 outputs
Outputs of similar age
#35,379
of 299,512 outputs
Outputs of similar age from Cancer Research
#10
of 150 outputs
Altmetric has tracked 22,113,391 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 17,634 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one has done particularly well, scoring higher than 93% 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 299,512 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 150 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 94% of its contemporaries.