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Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study

Overview of attention for article published in Lancet Oncology, October 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

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13 news outlets
blogs
1 blog
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15 X users
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4 Facebook pages

Citations

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173 Dimensions

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165 Mendeley
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Title
Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study
Published in
Lancet Oncology, October 2017
DOI 10.1016/s1470-2045(17)30597-1
Pubmed ID
Authors

Martin C Tammemagi, Heidi Schmidt, Simon Martel, Annette McWilliams, John R Goffin, Michael R Johnston, Garth Nicholas, Alain Tremblay, Rick Bhatia, Geoffrey Liu, Kam Soghrati, Kazuhiro Yasufuku, David M Hwang, Francis Laberge, Michel Gingras, Sergio Pasian, Christian Couture, John R Mayo, Paola V Nasute Fauerbach, Sukhinder Atkar-Khattra, Stuart J Peacock, Sonya Cressman, Diana Ionescu, John C English, Richard J Finley, John Yee, Serge Puksa, Lori Stewart, Scott Tsai, Ehsan Haider, Colm Boylan, Jean-Claude Cutz, Daria Manos, Zhaolin Xu, Glenwood D Goss, Jean M Seely, Kayvan Amjadi, Harmanjatinder S Sekhon, Paul Burrowes, Paul MacEachern, Stefan Urbanski, Don D Sin, Wan C Tan, Natasha B Leighl, Frances A Shepherd, William K Evans, Ming-Sound Tsao, Stephen Lam, PanCan Study Team

Abstract

Results from retrospective studies indicate that selecting individuals for low-dose CT lung cancer screening on the basis of a highly predictive risk model is superior to using criteria similar to those used in the National Lung Screening Trial (NLST; age, pack-year, and smoking quit-time). We designed the Pan-Canadian Early Detection of Lung Cancer (PanCan) study to assess the efficacy of a risk prediction model to select candidates for lung cancer screening, with the aim of determining whether this approach could better detect patients with early, potentially curable, lung cancer. We did this single-arm, prospective study in eight centres across Canada. We recruited participants aged 50-75 years, who had smoked at some point in their life (ever-smokers), and who did not have a self-reported history of lung cancer. Participants had at least a 2% 6-year risk of lung cancer as estimated by the PanCan model, a precursor to the validated PLCOm2012 model. Risk variables in the model were age, smoking duration, pack-years, family history of lung cancer, education level, body-mass index, chest x-ray in the past 3 years, and history of chronic obstructive pulmonary disease. Individuals were screened with low-dose CT at baseline (T0), and at 1 (T1) and 4 (T4) years post-baseline. The primary outcome of the study was incidence of lung cancer. This study is registered with ClinicalTrials.gov, number NCT00751660. 7059 queries came into the study coordinating centre and were screened for PanCan risk. 15 were duplicates, so 7044 participants were considered for enrolment. Between Sept 24, 2008, and Dec 17, 2010, we recruited and enrolled 2537 eligible ever-smokers. After a median follow-up of 5·5 years (IQR 3·2-6·1), 172 lung cancers were diagnosed in 164 individuals (cumulative incidence 0·065 [95% CI 0·055-0·075], incidence rate 138·1 per 10 000 person-years [117·8-160·9]). There were ten interval lung cancers (6% of lung cancers and 6% of individuals with cancer): one diagnosed between T0 and T1, and nine between T1 and T4. Cumulative incidence was significantly higher than that observed in NLST (4·0%; p<0·0001). Compared with 593 (57%) of 1040 lung cancers observed in NLST, 133 (77%) of 172 lung cancers in the PanCan Study were early stage (I or II; p<0·0001). The PanCan model was effective in identifying individuals who were subsequently diagnosed with early, potentially curable, lung cancer. The incidence of cancers detected and the proportion of early stage cancers in the screened population was higher than observed in previous studies. This approach should be considered for adoption in lung cancer screening programmes. Terry Fox Research Institute and Canadian Partnership Against Cancer.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 165 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 17%
Student > Ph. D. Student 15 9%
Other 13 8%
Student > Master 13 8%
Student > Postgraduate 10 6%
Other 34 21%
Unknown 52 32%
Readers by discipline Count As %
Medicine and Dentistry 60 36%
Biochemistry, Genetics and Molecular Biology 7 4%
Nursing and Health Professions 7 4%
Agricultural and Biological Sciences 5 3%
Immunology and Microbiology 3 2%
Other 17 10%
Unknown 66 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 110. 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 26 October 2021.
All research outputs
#383,116
of 25,382,440 outputs
Outputs from Lancet Oncology
#473
of 6,882 outputs
Outputs of similar age
#8,091
of 336,554 outputs
Outputs of similar age from Lancet Oncology
#10
of 129 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,882 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 35.2. 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 336,554 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 97% of its contemporaries.
We're also able to compare this research output to 129 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 92% of its contemporaries.