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Screen-detected subsolid pulmonary nodules: long-term follow-up and application of the PanCan lung cancer risk prediction model

Overview of attention for article published in British Journal of Radiology, February 2016
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Title
Screen-detected subsolid pulmonary nodules: long-term follow-up and application of the PanCan lung cancer risk prediction model
Published in
British Journal of Radiology, February 2016
DOI 10.1259/bjr.20160016
Pubmed ID
Authors

Henry Zhao, Henry M Marshall, Ian A Yang, Rayleen V Bowman, John Ayres, Jane Crossin, Melanie Lau, Richard E Slaughter, Stanley Redmond, Linda Passmore, Elizabeth McCaul, Deborah Courtney, Steven C Leong, Morgan Windsor, Paul V Zimmerman, Kwun M Fong

Abstract

To report the long-term follow-up of subsolid nodules (SSNs) detected in participants of a prospective low-dose computed tomography (CT) lung cancer screening cohort, and to investigate the utility of the PanCan model in stratifying risk in baseline SSNs. Participants underwent a baseline scan, two annual incidence scans, and further follow-up scans for detected nodules. All SSNs underwent minimum two years follow-up (unless resolved or resected). Risk of malignancy was estimated using the PanCan model; discrimination (area under the receiver operating characteristic curve) and calibration (Hosmer-Lemeshow goodness-of-fit test) were assessed. The Mann-Whitney-Wilcoxon test was used to compare estimated risk between groups. Seventy SSNs were detected in 41 (16.0%) out of 256 total participants. Median follow-up period was 25.5 months (range 2.0-74.0). Twenty-nine (41.4%) SSNs were transient. Five (7.1%) SSNs were resected, all found to be stage I lung adenocarcinoma, including one SSN stable in size for 3.0 years before growth was detected. The PanCan model had good discrimination for the 52 baseline SSNs (AUC=0.89, 95% CI 0.76-1); the Hosmer-Lemeshow goodness-of-fit test was nonsignificant (p=0.27). Estimated risk was significantly higher in the baseline SSNs found to be cancer versus those not found to be cancer after 2-6 years follow-up (p<0.01). Our findings support a long-term follow-up approach for screen-detected SSNs for three years or longer. The PanCan model appeared discriminatory and well-calibrated in this cohort. Advances in knowledge: The PanCan model may have utility in identifying low-risk SSNs which could be followed with less frequent CT scans.

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 4%
Unknown 27 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 18%
Student > Master 4 14%
Researcher 4 14%
Professor 3 11%
Lecturer 2 7%
Other 5 18%
Unknown 5 18%
Readers by discipline Count As %
Medicine and Dentistry 14 50%
Computer Science 2 7%
Biochemistry, Genetics and Molecular Biology 1 4%
Mathematics 1 4%
Business, Management and Accounting 1 4%
Other 2 7%
Unknown 7 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 February 2016.
All research outputs
#16,581,410
of 25,377,790 outputs
Outputs from British Journal of Radiology
#2,112
of 3,297 outputs
Outputs of similar age
#176,581
of 311,617 outputs
Outputs of similar age from British Journal of Radiology
#23
of 47 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,297 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
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 311,617 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 47 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 51% of its contemporaries.