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. 2023 Aug;43(8):877-895.
doi: 10.1002/cac2.12463. Epub 2023 Jul 6.

Lung cancer risk score for ever and never smokers in China

Zhimin Ma  1   2   3 , Jun Lv  4   5 , Meng Zhu  1   2 , Canqing Yu  4 , Hongxia Ma  1   2 , Guangfu Jin  1   2 , Yu Guo  6 , Zheng Bian  6 , Ling Yang  7 , Yiping Chen  7 , Zhengming Chen  7 , Zhibin Hu  1   2 , Liming Li  4   7 , Hongbing Shen  1   2   8
Affiliations

Lung cancer risk score for ever and never smokers in China

Zhimin Ma et al. Cancer Commun (Lond). 2023 Aug.

Abstract

Background: Most lung cancer risk prediction models were developed in European and North-American cohorts of smokers aged ≥ 55 years, while less is known about risk profiles in Asia, especially for never smokers or individuals aged < 50 years. Hence, we aimed to develop and validate a lung cancer risk estimate tool for ever and never smokers across a wide age range.

Methods: Based on the China Kadoorie Biobank cohort, we first systematically selected the predictors and explored the nonlinear association of predictors with lung cancer risk using restricted cubic splines. Then, we separately developed risk prediction models to construct a lung cancer risk score (LCRS) in 159,715 ever smokers and 336,526 never smokers. The LCRS was further validated in an independent cohort over a median follow-up of 13.6 years, consisting of 14,153 never smokers and 5,890 ever smokers.

Results: A total of 13 and 9 routinely available predictors were identified for ever and never smokers, respectively. Of these predictors, cigarettes per day and quit years showed nonlinear associations with lung cancer risk (Pnon-linear < 0.001). The curve of lung cancer incidence increased rapidly above 20 cigarettes per day and then was relatively flat until approximately 30 cigarettes per day. We also observed that lung cancer risk declined sharply within the first 5 years of quitting, and then continued to decrease but at a slower rate in the subsequent years. The 6-year area under the receiver operating curve for the ever and never smokers' models were respectively 0.778 and 0.733 in the derivation cohort, and 0.774 and 0.759 in the validation cohort. In the validation cohort, the 10-year cumulative incidence of lung cancer was 0.39% and 2.57% for ever smokers with low (< 166.2) and intermediate-high LCRS (≥ 166.2), respectively. Never smokers with a high LCRS (≥ 21.2) had a higher 10-year cumulative incidence rate than those with a low LCRS (< 21.2; 1.05% vs. 0.22%). An online risk evaluation tool (LCKEY; http://ccra.njmu.edu.cn/lckey/web) was developed to facilitate the use of LCRS.

Conclusions: The LCRS can be an effective risk assessment tool designed for ever and never smokers aged 30 to 80 years.

Keywords: early-onset cancer; lung cancer; lung cancer screening; never smokers; prediction model.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

FIGURE 1
FIGURE 1
Study design and eligible participants’ selection procedure. (A) Eligible participants’ selection procedure in the CKB cohort. (B) Eligible participants’ selection procedure in the Changzhou cohort. Abbreviation: CKB, China Kadoorie Biobank.
FIGURE 2
FIGURE 2
Linear and non‐linear association between predictors and lung cancer risk. The linear association between age and lung cancer risk using restricted cubic splines in ever smokers (A) and never smokers (B). The linear association between height and lung cancer risk using restricted cubic splines in ever smokers (C) and never smokers (D). The linear association between BMI and lung cancer risk using restricted cubic splines in ever smokers (E) and never smokers (F). The non‐linear association between cigarettes per day and lung cancer risk using restricted cubic splines in ever smokers (G). The non‐linear association between smoking years and lung cancer risk using restricted cubic splines in ever smokers (H). The non‐linear association between quit years and lung cancer risk using restricted cubic splines in ever smokers (I). Abbreviations: BMI, body‐mass index; HR, hazard ratio.
FIGURE 3
FIGURE 3
Distribution, discrimination, and calibration of the LCRS in the CKB and Changzhou cohorts. Distribution of the LCRS across incident lung cancer cases and lung cancer‐free participants during follow‐up in the CKB (A and B) and Changzhou cohorts (G and H). Receiver operating characteristic curve at six years in the CKB cohort (C and D) and Changzhou cohort (I and J). The observed 6‐year probability of lung cancer with 95% CIs was estimated by the Kaplan‐Meier method within deciles of LCRS predicted probability in the CKB (E and F) and Changzhou cohorts (K and L). Abbreviations: AUC, area under the receiver operating curve; CI, confidence interval; CKB, China Kadoorie Biobank; LCRS, lung cancer risk score.
FIGURE 4
FIGURE 4
The association of the LCRS with incident lung cancer risk in the CKB cohort. The linear association of LCRS and lung cancer risk using restricted cubic splines in ever smokers (A) and never smokers (B) in the CKB cohort. The linear association of LCRS and lung cancer risk using restricted cubic splines in ever smokers (C) and never smokers (D) in the Changzhou cohort. Abbreviations: CKB, China Kadoorie Biobank; HR, hazard ratio; LCRS, lung cancer risk score.
FIGURE 5
FIGURE 5
Inverted Kaplan‐Meier plot of incident lung cancer in the CKB and Changzhou cohorts. Ever smokers were classified into low (LCRS < 166.2) and intermediate‐high risk groups (LCRS ≥ 166.2); and never smokers were divided into low (LCRS < 21.2) and high (LCRS ≥ 21.2) risk groups. Abbreviations: CKB, China Kadoorie Biobank; LCRS, lung cancer risk score.
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