Open AccessDevelopment of a risk prediction model for lung cancer: The Japan Public Health Center‐based Prospective Study
Author(s) -
Charvat Hadrien,
Sasazuki Shizuka,
Shimazu Taichi,
Budhathoki Sanjeev,
Inoue Manami,
Iwasaki Motoki,
Sawada Norie,
Yamaji Taiki,
Tsugane Shoichiro
Publication year - 2018
Publication title -
cancer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.035
H-Index - 141
eISSN - 1349-7006
pISSN - 1347-9032
DOI - 10.1111/cas.13509
Subject(s) - medicine , lung cancer , cohort , hazard ratio , proportional hazards model , population , demography , cohort study , prospective cohort study , smoking cessation , risk assessment , confidence interval , environmental health , pathology , computer science , computer security , sociology
Although the impact of tobacco consumption on the occurrence of lung cancer is well‐established, risk estimation could be improved by risk prediction models that consider various smoking habits, such as quantity, duration, and time since quitting. We constructed a risk prediction model using a population of 59 161 individuals from the Japan Public Health Center ( JPHC ) Study Cohort II . A parametric survival model was used to assess the impact of age, gender, and smoking‐related factors (cumulative smoking intensity measured in pack‐years, age at initiation, and time since cessation). Ten‐year cumulative probability of lung cancer occurrence estimates were calculated with consideration of the competing risk of death from other causes. Finally, the model was externally validated using 47 501 individuals from JPHC Study Cohort I. A total of 1210 cases of lung cancer occurred during 986 408 person‐years of follow‐up. We found a dose‐dependent effect of tobacco consumption with hazard ratios for current smokers ranging from 3.78 (2.00‐7.16) for cumulative consumption ≤15 pack‐years to 15.80 (9.67‐25.79) for >75 pack‐years. Risk decreased with time since cessation. Ten‐year cumulative probability of lung cancer occurrence estimates ranged from 0.04% to 11.14% in men and 0.07% to 6.55% in women. The model showed good predictive performance regarding discrimination (cross‐validated c ‐index = 0.793) and calibration (cross‐validated χ 2 = 6.60; P ‐value = .58). The model still showed good discrimination in the external validation population ( c ‐index = 0.772). In conclusion, we developed a prediction model to estimate the probability of developing lung cancer based on age, gender, and tobacco consumption. This model appears useful in encouraging high‐risk individuals to quit smoking and undergo increased surveillance.