ArticleComputer methods and programs in biomedicine2022
Prediction of lung cancer risk based on age and smoking history.
Article in Computer methods and programs in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Observational
- Age-related differences in clinical characteristics and survival in lung cancer.Frontiers in oncology · 2026Article
- The analysis of postoperative wound healing, infection risk prediction, and nursing intervention strategies for elderly patients with lung cancer.BMC geriatrics · 2025Article
- Development, deployment, and feature interpretability of a three-class prediction model for pulmonary diseases.Insights into imaging · 2025Article
- Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study.BMC cancer · 2025Article
- Accelerated biological aging as potential mediator in the relationship between central obesity and lung cancer risk.Frontiers in aging · 2025Article
- Retrospective Study of Lung Cancer: Evolution in 16 Years in the Burgos Region.Journal of clinical medicine · 2024Article
- Photonanozyme-Kras-ribosome combination treatment of non-small cell lung cancer after COVID-19.Frontiers in immunology · 2024Review
- Association between cigarette smoking history, metabolic phenotypes, andJournal of thoracic disease · 2023Article
- Nomogram combining clinical and radiological characteristics for predicting the malignant probability of solitary pulmonary nodules measuring ≤ 2 cm.Frontiers in oncology · 2023Article
- Non-small cell lung cancer in China.Cancer communications (London, England) · 2022Review
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Authors and funding
4 authors.
Funding
Abstract
BACKGROUND AND
objectiveThe CISNET models provide predictions for dying of lung cancer in any year of life as a function of age and smoking history, but their predictions are quite variable and the models themselves can be complex to implement. Our goal was to develop a simple empirical model of the risk of dying of lung cancer that is mathematically constrained to produce biologically appropriate probability predictions as a function of current age, smoking start age, quit age, and smoking intensity.
methodsThe six adjustable parameters of the model were evaluated by fitting its predictions of cancer death risk versus age to the mean of published predictions made by the CISNET models for the never smoker and for six different scenarios of lifetime smoking burden.
resultsThe mean RMS fitting error of the model was 6.16 × 10
conclusionsOur simple model of the risk of dying of lung cancer in any given year of life as a function of smoking history is easily implemented and thus may serve as a useful tool in situations where the mortality risks of smoking need to be estimated.
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