Prediction of lung cancer risk based on age and smoking history. (April 2022)

• Record Type: Journal Article
• Authors: Bates, Jason H.T.; Hamlington, Katharine L.; Garrison, Garth; Kinsey, C. Matthew
• Is Part Of: Computer methods and programs in biomedicine. Volume 216(2022)
• Publication Date: 2022-04
• Subjects: RMS root mean square -- CISNET cancer intervention and surveillance modeling network; CISNET models -- Probability -- Cigarettes -- Smoking intensity -- Start age -- Quit age; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106660
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlight: Although a number of computational models exist for predicting the risk of dying of lung cancer in any year of life as a function of age and smoking history, their predictions are quite variable and the models themselves can be complex to implement. We have developed 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. This simple model is easily implemented and may serve as a useful tool in situations where the mortality risks of smoking need to be estimated. Abstract: Background and Objective: The 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. Methods: The 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. Results: The mean RMS fitting error of the model was 6.16 × 10 -2 (% risk of dying of cancer per year of life) between 55 and 80 years of age. The model predictions increased monotonically with current age, quit age and smoking intensity, and decreased with increasing start age. Conclusions: Our 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.