The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The LLP risk model: an individual risk prediction model for lung cancer
Open accessAbstract
Using a model-based approach, we estimated the probability that an individual, with a specified combination of risk factors, would develop lung cancer within a 5-year period. Data from 579 lung cancer cases and 1157 age- and sex-matched population-based controls were available for this analysis. Significant risk factors were fitted into multivariate conditional logistic regression models. The final multivariate model was combined with age-standardised lung cancer incidence data to calculate absolute risk estimates. Combinations of lifestyle risk factors were modelled to create risk profiles. For example, a 77-year-old male non-smoker, with a family history of lung cancer (early onset) and occupational exposure to asbestos has an absolute risk of 3.17% (95% CI, 1.67-5.95). Choosing a 2.5% cutoff to trigger increased surveillance, gave a sensitivity of 0.62 and specificity of 0.70, while a 6.0% cutoff gave a sensitivity of 0.34 and specificity of 0.90. A 10-fold cross validation produced an AUC statistic of 0.70, indicating good discrimination.If independent validation studies confirm these results, the LLP risk models' application as the first stage in an early detection strategy is a logical evolution in patient care.
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Gail, M. H., Brinton, L. A., Byar, D. P., Corle, D. K., Green, S. B., Schairer, C., & Mulvihill, J. J. (1989) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/81.24.1879 · Cited by 3,334
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Jaklitsch, M. T., Jacobson, F. L., Austin, J. H. M., Field, J. K., Jett, J. R., Keshavjee, S., MacMahon, H., Mulshine, J. L., Munden, R. F., Salgia, R., et al. (2012) · The Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2012.05.060 · Cited by 608
Self-citationEuropean 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
Self-citationAdams, 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
Mikhael, P. G., Wohlwend, J., Yala, A., Karstens, L., Xiang, J., Takigami, A. K., Bourgouin, P. P., Chan, P., Mrah, S., Amayri, W., et al. (2023) · Journal of Clinical Oncology · doi:10.1200/jco.22.01345 · Cited by 288
Metadata
- Authors
- Cassidy, A., Myles, J. P., van Tongeren, M., Page, R. D., Liloglou, T., Duffy, S. W., & Field, J. K.
- Venue
- British Journal of Cancer
- Published
- 2007-12-18
- Volume
- 98
- Issue
- 2
- Pages
- 270-276
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- OpenAlex
- W2033649338
- Cited by
- 490
- References
- 1
- FWCI
- 5.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Cassidy, A., Myles, J. P., van Tongeren, M., Page, R. D., Liloglou, T., Duffy, S. W., & Field, J. K. (2008). The LLP risk model: an individual risk prediction model for lung cancer. British Journal of Cancer. https://doi.org/10.1038/sj.bjc.6604158
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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.
