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Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening
- Source :
- European Respiratory Journal, 58, European Respiratory Journal, 58, 3, European respiratory journal, 58(3):2003386. European Respiratory Society, The European Respiratory Journal, 58(3):2003386. European Respiratory Society
- Publication Year :
- 2020
-
Abstract
- ObjectivesCombined assessment of cardiovascular disease (CVD), COPD and lung cancer may improve the effectiveness of lung cancer screening in smokers. The aims were to derive and assess risk models for predicting lung cancer incidence, CVD mortality and COPD mortality by combining quantitative computed tomography (CT) measures from each disease, and to quantify the added predictive benefit of self-reported patient characteristics given the availability of a CT scan.MethodsA survey model (patient characteristics only), CT model (CT information only) and final model (all variables) were derived for each outcome using parsimonious Cox regression on a sample from the National Lung Screening Trial (n=15 000). Validation was performed using Multicentric Italian Lung Detection data (n=2287). Time-dependent measures of model discrimination and calibration are reported.ResultsAge, mean lung density, emphysema score, bronchial wall thickness and aorta calcium volume are variables that contributed to all final models. Nodule features were crucial for lung cancer incidence predictions but did not contribute to CVD and COPD mortality prediction. In the derivation cohort, the lung cancer incidence CT model had a 5-year area under the receiver operating characteristic curve of 82.5% (95% CI 80.9–84.0%), significantly inferior to that of the final model (84.0%, 82.6–85.5%). However, the addition of patient characteristics did not improve the lung cancer incidence model performance in the validation cohort (CT model 80.1%, 74.2–86.0%; final model 79.9%, 73.9–85.8%). Similarly, the final CVD mortality model outperformed the other two models in the derivation cohort (survey model 74.9%, 72.7–77.1%; CT model 76.3%, 74.1–78.5%; final model 79.1%, 77.0–81.2%), but not the validation cohort (survey model 74.8%, 62.2–87.5%; CT model 72.1%, 61.1–83.2%; final model 72.2%, 60.4–84.0%). Combining patient characteristics and CT measures provided the largest increase in accuracy for the COPD mortality final model (92.3%, 90.1–94.5%) compared to either other model individually (survey model 87.5%, 84.3–90.6%; CT model 87.9%, 84.8–91.0%), but no external validation was performed due to a very low event frequency.ConclusionsCT measures of CVD and COPD provides small but reproducible improvements to nodule-based lung cancer risk prediction accuracy from 3 years onwards. Self-reported patient characteristics may not be of added predictive value when CT information is available.
- Subjects :
- Pulmonary and Respiratory Medicine
medicine.medical_specialty
Lung Neoplasms
Vascular damage Radboud Institute for Health Sciences [Radboudumc 16]
03 medical and health sciences
All institutes and research themes of the Radboud University Medical Center
0302 clinical medicine
medicine
Humans
030212 general & internal medicine
Quantitative computed tomography
Lung cancer
Lung
Early Detection of Cancer
COPD
medicine.diagnostic_test
Receiver operating characteristic
Proportional hazards model
business.industry
Incidence (epidemiology)
medicine.disease
Women's cancers Radboud Institute for Health Sciences [Radboudumc 17]
030228 respiratory system
National Lung Screening Trial
Radiology
business
Tomography, X-Ray Computed
Lung cancer screening
Biomarkers
Rare cancers Radboud Institute for Health Sciences [Radboudumc 9]
Subjects
Details
- ISSN :
- 13993003 and 09031936
- Volume :
- 58
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- The European respiratory journal
- Accession number :
- edsair.doi.dedup.....7194cbedd66ca3033053a38813eb8555