Dataset Information

Lung cancer detectability by test, histology, stage, and gender: estimates from the NLST and the PLCO trials.

ABSTRACT:

Background

Implementing optimal lung cancer screening programs requires knowledge of the natural history and detectability of lung cancer. This information can be derived from the results of clinical trials with the aid of microsimulation models.

Methods

Data from the Surveillance, Epidemiology, and End Results (SEER) program and individual-level data from the National Lung Screening Trial (NLST) and the Prostate, Lung, Colon, and Ovarian Cancer Screening trial (PLCO) were used to investigate the sensitivity (by histology and stage) of CT and chest radiography (CXR) and the mean preclinical sojourn time (MPST) of lung cancer (by gender, histology, and stage). The MISCAN-Lung model was used to reproduce the lung cancer incidence by method of detection (clinically or screen-detected), gender, histology, and stage in both trials and SEER, by calibrating CT and CXR sensitivity and natural history parameters.

Results

CT sensitivity ranges from 8.83% to 99.35% and CXR sensitivity from 2.51% to 97.31%, depending on histology and stage. CT sensitivity for stage IA is more than 3-fold higher compared with CXR, for all histologies. The total MPST estimates for lung cancer progressing through preclinical stages IA to IV range from 3.09 to 5.32 years for men and 3.35 to 6.01 years for women. The largest difference in total MPST between genders was estimated for adenocarcinoma.

Conclusions

We estimate longer MPSTs for lung cancer compared with previous research, suggesting a greater window of opportunity for lung cancer screening.

Impact

This study provides detailed insights into the natural history of lung cancer and CT screening effectiveness.

SUBMITTER: Ten Haaf K

PROVIDER: S-EPMC4357842 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Publications

Lung cancer detectability by test, histology, stage, and gender: estimates from the NLST and the PLCO trials.

Ten Haaf Kevin K van Rosmalen Joost J de Koning Harry J HJ

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20141013 1

<h4>Background</h4>Implementing optimal lung cancer screening programs requires knowledge of the natural history and detectability of lung cancer. This information can be derived from the results of clinical trials with the aid of microsimulation models.<h4>Methods</h4>Data from the Surveillance, Epidemiology, and End Results (SEER) program and individual-level data from the National Lung Screening Trial (NLST) and the Prostate, Lung, Colon, and Ovarian Cancer Screening trial (PLCO) were used to ...[more]

PMID: 25312998

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Project description:BackgroundA reliable risk prediction model is critically important for identifying individuals with high risk of developing lung cancer as candidates for low-dose chest computed tomography (LDCT) screening. Leveraging a cutting-edge machine learning technique that accommodates a wide list of questionnaire-based predictors, we sought to optimize and validate a lung cancer prediction model.MethodsWe developed an Optimized early Warning model for Lung cancer risk (OWL) using the XGBoost algorithm with 323,344 participants from the England area in UK Biobank (training set), and independently validated it with 93,227 participants from UKB Scotland and Wales area (validation set 1), as well as 70,605 and 66,231 participants in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial (PLCO) control and intervention subpopulations, respectively (validation sets 2 & 3) and 23,138 and 18,669 participants in the United States National Lung Screening Trial (NLST) control and intervention subpopulations, respectively (validation sets 4 & 5). By comparing with three competitive prediction models, i.e., PLCO modified 2012 (PLCOm2012), PLCO modified 2014 (PLCOall2014), and the Liverpool Lung cancer Project risk model version 3 (LLPv3), we assessed the discrimination of OWL by the area under receiver operating characteristic curve (AUC) at the designed time point. We further evaluated the calibration using relative improvement in the ratio of expected to observed lung cancer cases (RIEO), and illustrated the clinical utility by the decision curve analysis.FindingsFor general population, with validation set 1, OWL (AUC = 0.855, 95% CI: 0.829-0.880) presented a better discriminative capability than PLCOall2014 (AUC = 0.821, 95% CI: 0.794-0.848) (p < 0.001); with validation sets 2 & 3, AUC of OWL was comparable to PLCOall2014 (AUCPLCOall2014-AUCOWL < 1%). For ever-smokers, OWL outperformed PLCOm2012 and PLCOall2014 among ever-smokers in validation set 1 (AUCOWL = 0.842, 95% CI: 0.814-0.871; AUCPLCOm2012 = 0.792, 95% CI: 0.760-0.823; AUCPLCOall2014 = 0.791, 95% CI: 0.760-0.822, all p < 0.001). OWL remained comparable to PLCOm2012 and PLCOall2014 in discrimination (AUC difference from -0.014 to 0.008) among the ever-smokers in validation sets 2 to 5. In all the validation sets, OWL outperformed LLPv3 among the general population and the ever-smokers. Of note, OWL showed significantly better calibration than PLCOm2012, PLCOall2014 (RIEO from 43.1% to 92.3%, all p < 0.001), and LLPv3 (RIEO from 41.4% to 98.7%, all p < 0.001) in most cases. For clinical utility, OWL exhibited significant improvement in average net benefits (NB) over PLCOall2014 in validation set 1 (NB improvement: 32, p < 0.001); among ever smokers of validation set 1, OWL (average NB = 289) retained significant improvement over PLCOm2012 (average NB = 213) (p < 0.001). OWL had equivalent NBs with PLCOm2012 and PLCOall2014 in PLCO and NLST populations, while outperforming LLPv3 in the three populations.InterpretationOWL, with a high degree of predictive accuracy and robustness, is a general framework with scientific justifications and clinical utility that can aid in screening individuals with high risks of lung cancer.FundingNational Natural Science Foundation of China, the US NIH.

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Dataset Information

Lung cancer detectability by test, histology, stage, and gender: estimates from the NLST and the PLCO trials.

Background

Methods

Results

Conclusions

Impact

Publications

Lung cancer detectability by test, histology, stage, and gender: estimates from the NLST and the PLCO trials.

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