Title |
Screen-detected subsolid pulmonary nodules: long-term follow-up and application of the PanCan lung cancer risk prediction model
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Published in |
British Journal of Radiology, February 2016
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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. |
X Demographics
Geographical breakdown
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Canada | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 50% |
Scientists | 1 | 50% |
Mendeley readers
Geographical breakdown
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Canada | 1 | 4% |
Unknown | 27 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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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 % |
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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% |