In:
Thorax, BMJ, Vol. 73, No. 8 ( 2018-08), p. 741-747
Abstract:
New nodules after baseline are regularly found in low-dose CT lung cancer screening and have a high lung cancer probability. It is unknown whether morphological and location characteristics can improve new nodule risk stratification by size. Methods Solid non-calcified nodules detected during incidence screening rounds of the randomised controlled Dutch-Belgian lung cancer screening (NELSON) trial and registered as new or previously below detection limit (15 mm 3 ) were included. A multivariate logistic regression analysis with lung cancer as outcome was performed, including previously established volume cut-offs ( 〈 30 mm 3 , 30– 〈 200 mm 3 and ≥200 mm 3 ) and nodule characteristics (location, distribution, shape, margin and visibility 〈 15 mm 3 in retrospect). Results Overall, 1280 new nodules were included with 73 (6%) being lung cancer. Of nodules ≥30 mm 3 at detection and visible 〈 15 mm 3 in retrospect, 22% (6/27) were lung cancer. Discrimination based on volume cut-offs (area under the receiver operating characteristic curve (AUC): 0.80, 95% CI 0.75 to 0.84) and continuous volume (AUC: 0.82, 95% CI 0.77 to 0.87) was similar. After adjustment for volume cut-offs, only location in the right upper lobe (OR 2.0, P=0.012), central distribution (OR 2.4, P=0.001) and visibility 〈 15 mm 3 in retrospect (OR 4.7, P=0.003) remained significant predictors for lung cancer. The Hosmer-Lemeshow test (P=0.75) and assessment of bootstrap calibration curves indicated adequate model fit. Discrimination based on the continuous model probability (AUC: 0.85, 95% CI 0.81 to 0.89) was superior to volume cut-offs alone, but when stratified into three risk groups (AUC: 0.82, 95% CI 0.78 to 0.86), discrimination was similar. Conclusion Contrary to morphological nodule characteristics, growth-independent characteristics may further improve volume-based new nodule lung cancer prediction, but in a three-category stratification approach, this is limited. Trial registration number ISRCTN63545820 ; pre-results.
Type of Medium:
Online Resource
ISSN:
0040-6376
,
1468-3296
DOI:
10.1136/thoraxjnl-2017-211376
DOI:
10.1136/thoraxjnl-2017-211376.supp1
Language:
English
Publisher:
BMJ
Publication Date:
2018
detail.hit.zdb_id:
1481491-2
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