The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Selection Criteria for Lung-Cancer Screening
Open accessAbstract
BACKGROUND: The National Lung Screening Trial (NLST) used risk factors for lung cancer (e.g., ≥30 pack-years of smoking and <15 years since quitting) as selection criteria for lung-cancer screening. Use of an accurate model that incorporates additional risk factors to select persons for screening may identify more persons who have lung cancer or in whom lung cancer will develop. METHODS: We modified the 2011 lung-cancer risk-prediction model from our Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to ensure applicability to NLST data; risk was the probability of a diagnosis of lung cancer during the 6-year study period. We developed and validated the model (PLCO(M2012)) with data from the 80,375 persons in the PLCO control and intervention groups who had ever smoked. Discrimination (area under the receiver-operating-characteristic curve [AUC]) and calibration were assessed. In the validation data set, 14,144 of 37,332 persons (37.9%) met NLST criteria. For comparison, 14,144 highest-risk persons were considered positive (eligible for screening) according to PLCO(M2012) criteria. We compared the accuracy of PLCO(M2012) criteria with NLST criteria to detect lung cancer. Cox models were used to evaluate whether the reduction in mortality among 53,202 persons undergoing low-dose computed tomographic screening in the NLST differed according to risk. RESULTS: The AUC was 0.803 in the development data set and 0.797 in the validation data set. As compared with NLST criteria, PLCO(M2012) criteria had improved sensitivity (83.0% vs. 71.1%, P<0.001) and positive predictive value (4.0% vs. 3.4%, P=0.01), without loss of specificity (62.9% and. 62.7%, respectively; P=0.54); 41.3% fewer lung cancers were missed. The NLST screening effect did not vary according to PLCO(M2012) risk (P=0.61 for interaction). CONCLUSIONS: The use of the PLCO(M2012) model was more sensitive than the NLST criteria for lung-cancer detection.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
The National Lung Screening Trial Research Team (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1102873 · Cited by 11,064
Cited by
Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement
US Preventive Services Task Force, Krist, A. H., Davidson, K. W., Mangione, C. M., Barry, M. J., Cabana, M., Caughey, A. B., Davis, E. M., Donahue, K. E., Doubeni, C. A., et al. (2021) · JAMA · doi:10.1001/jama.2021.1117 · Cited by 1,816
Leening, M. J. G., Vedder, M. M., Witteman, J. C. M., Pencina, M. J., & Steyerberg, E. W. (2014) · Annals of Internal Medicine · doi:10.7326/m13-1522 · Cited by 594
European position statement on lung cancer screening
Oudkerk, M., Devaraj, A., Vliegenthart, R., Henzler, T., Prosch, H., Heussel, C. P., Bastarrika, G., Sverzellati, N., Mascalchi, M., Delorme, S., et al. (2017) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30861-6 · Cited by 587
Adams, S. J., Stone, E., Baldwin, D. R., Vliegenthart, R., Lee, P., & Fintelmann, F. J. (2023) · The Lancet · doi:10.1016/s0140-6736(22)01694-4 · Cited by 467
Mazzone, P. J., Silvestri, G. A., Patel, S., Kanne, J. P., Kinsinger, L. S., Wiener, R. S., Soo Hoo, G., & Detterbeck, F. C. (2018) · Chest · doi:10.1016/j.chest.2018.01.016 · Cited by 348
Self-citation
Metadata
- Authors
- Tammemägi, M. C., Katki, H. A., Hocking, W. G., Church, T. R., Caporaso, N., Kvale, P. A., Chaturvedi, A. K., Silvestri, G. A., Riley, T. L., Commins, J., & Berg, C. D.
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2013-02-21
- Volume
- 368
- Issue
- 8
- Pages
- 728-736
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W2012944782
- Cited by
- 1,029
- References
- 1
- FWCI
- 48.47
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Selection Criteria for Lung-Cancer Screening. https://app.cochat.ai/papers/10.1056/nejmoa1211776
- The paper
- Tammemägi, M. C., Katki, H. A., Hocking, W. G., Church, T. R., Caporaso, N., Kvale, P. A., Chaturvedi, A. K., Silvestri, G. A., Riley, T. L., Commins, J., & Berg, C. D. (2013). Selection Criteria for Lung-Cancer Screening. New England Journal of Medicine. https://doi.org/10.1056/nejmoa1211776
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.