Lung Cancer Risk Prediction Models for Asian Ever-Smokers.

Pubmed ID: 37944700

Journal: Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer

Publication Date: Nov. 7, 2023

Affiliation: Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee. Electronic address: xiao-ou.shu@vanderbilt.edu.

Authors: Wada K, You SL, Ahsan H, Etemadi A, Sakata R, Abe SK, Tsuji I, Kanemura S, Ohrr H, Sugawara Y, Saito E, Yi SW, Ahn YO, Wen W, Kim J, Park SK, Tamakoshi A, Shin A, Pednekar MS, Blechter B, Rahman MS, Gupta PC, Nagata C, Zahed H, Malekzadeh R, Islam MR, Kimura T, Sheikh M, Lu Y, Robbins HA, Yang JJ, Kweon SS, Shin MH, Chen CJ, Kang D, Chia KS, Ito H, Matsuo K, Lan Q, Wang R, Koh WP, Qiao YL, Yuan JM, Inoue M, Gao YT, Rothman N, Cai H, Boffetta P, Zheng W, Shu XO

Cite As: Yang JJ, Wen W, Zahed H, Zheng W, Lan Q, Abe SK, Rahman MS, Islam MR, Saito E, Gupta PC, Tamakoshi A, Koh WP, Gao YT, Sakata R, Tsuji I, Malekzadeh R, Sugawara Y, Kim J, Ito H, Nagata C, You SL, Park SK, Yuan JM, Shin MH, Kweon SS, Yi SW, Pednekar MS, Kimura T, Cai H, Lu Y, Etemadi A, Kanemura S, Wada K, Chen CJ, Shin A, Wang R, Ahn YO, Shin MH, Ohrr H, Sheikh M, Blechter B, Ahsan H, Boffetta P, Chia KS, Matsuo K, Qiao YL, Rothman N, Inoue M, Kang D, Robbins HA, Shu XO. Lung Cancer Risk Prediction Models for Asian Ever-Smokers. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer. 2023 Nov 7.

Related Projects:

Evaluating risk prediction models for use in lung cancer screening across diverse populations around the world (Subproject)

Abstract

BACKGROUND: Although lung cancer prediction models are widely used to support risk-based screening, their performance outside Western populations remains uncertain. This study aims to evaluate the performance of 11 existing risk prediction models in multiple Asian populations and to refit prediction models for Asians. METHODS: In a pooled analysis of 186,458 Asian ever-smokers from 19 prospective cohorts, we assessed calibration (expected-to-observed ratio) and discrimination (area under the receiver operating characteristic curve [AUC]) for each model. In addition, we developed the "Shanghai models" to better refine risk models for Asians on the basis of two well-characterized population-based prospective cohorts and externally validated them in other Asian cohorts. RESULTS: Among the 11 models, the Lung Cancer Death Risk Assessment Tool yielded the highest AUC (AUC [95% confidence interval (CI)] = 0.71 [0.67-0.74] for lung cancer death and 0.69 [0.67-0.72] for lung cancer incidence) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model had good calibration overall (expected-to-observed ratio [95% CI] = 1.06 [0.90-1.25]). Nevertheless, these models substantially underestimated lung cancer risk among Asians who reported less than 10 smoking pack-years or stopped smoking more than or equal to 20 years ago. The Shanghai models were found to have marginal improvement overall in discrimination (AUC [95% CI] = 0.72 [0.69-0.74] for lung cancer death and 0.70 [0.67-0.72] for lung cancer incidence) but consistently outperformed the selected Western models among low-intensity smokers and long-term quitters. CONCLUSIONS: The Shanghai models had comparable performance overall to the best existing models, but they improved much in predicting the lung cancer risk of low-intensity smokers and long-term quitters in Asia.

Last Updated: 14 Mar, 2025