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Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy

Overview of attention for article published in BMC Medical Genomics, May 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

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4 patents
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1 Facebook page

Citations

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61 Dimensions

Readers on

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73 Mendeley
Title
Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy
Published in
BMC Medical Genomics, May 2015
DOI 10.1186/s12920-015-0091-3
Pubmed ID
Authors

Duncan H Whitney, Michael R Elashoff, Kate Porta-Smith, Adam C Gower, Anil Vachani, J Scott Ferguson, Gerard A Silvestri, Jerome S Brody, Marc E Lenburg, Avrum Spira

Abstract

The gene expression profile of cytologically-normal bronchial airway epithelial cells has previously been shown to be altered in patients with lung cancer. Although bronchoscopy is often used for the diagnosis of lung cancer, its sensitivity is imperfect, especially for small and peripheral suspicious lesions. In this study, we derived a gene expression classifier from bronchoscopically-obtained airway epithelial cells that detects the presence of cancer in current and former smokers undergoing bronchoscopy for suspect lung cancer and evaluated its sensitivity to detect lung cancer among patients from an independent cohort. We collected bronchial epithelial cells (BEC) from the mainstem bronchus of 299 current or former smokers (223 cancer-positive and 76 cancer-free subjects) undergoing bronchoscopy for suspected lung cancer in a prospective, multi-center study. RNA from these samples was run on gene expression microarrays for training a gene-expression classifier. A logistic regression model was built to predict cancer status, and the finalized classifier was validated in an independent cohort from a previous study. We found 232 genes whose expression levels in the bronchial airway are associated with lung cancer. We then built a classifier based on the combination of 17 cancer genes, gene expression predictors of smoking status, smoking history, and gender, plus patient age. This classifier had a ROC curve AUC of 0.78 (95% CI, 0.70-0.86) in patients whose bronchoscopy did not lead to a diagnosis of lung cancer (n = 134). In the validation cohort, the classifier had a similar AUC of 0.81 (95% CI, 0.73-0.88) in this same subgroup (n = 118). The classifier performed similarly across a range of mass sizes, cancer histologies and stages. The negative predictive value was 94% (95% CI, 83-99%) in subjects without bronchoscopy-detected lung cancer. We developed a gene expression classifier measured in bronchial airway epithelial cells that is able to accurately identify lung cancer in current and former smokers who have undergone bronchoscopy for suspicion of lung cancer. Due to the high NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing in patients whose bronchoscopy is non diagnostic.

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
United States 1 1%
Unknown 71 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 23%
Researcher 16 22%
Other 9 12%
Student > Master 8 11%
Professor > Associate Professor 4 5%
Other 7 10%
Unknown 12 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 25%
Medicine and Dentistry 17 23%
Agricultural and Biological Sciences 12 16%
Computer Science 3 4%
Engineering 2 3%
Other 5 7%
Unknown 16 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 30 August 2022.
All research outputs
#4,602,937
of 22,803,211 outputs
Outputs from BMC Medical Genomics
#220
of 1,223 outputs
Outputs of similar age
#58,913
of 264,547 outputs
Outputs of similar age from BMC Medical Genomics
#6
of 23 outputs
Altmetric has tracked 22,803,211 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,223 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 81% 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 264,547 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 77% of its contemporaries.
We're also able to compare this research output to 23 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 69% of its contemporaries.