Lung Cancer Risk Prediction Models for Asian Ever-Smokers

Cost-effectiveness of risk model-based lung cancer screening in smokers and nonsmokers in China

BACKGROUND

China bears the largest global burden of lung cancer, with a striking 40% of cases occurring in individuals who have never smoked. While the mortality-reducing benefits of low-dose computed tomography (LDCT) for lung cancer screening are established, the quest for an optimal screening strategy continues, considering the potential adverse effects of LDCT. The Chinese NCC-LCm2021 model was developed based on a nationwide population to identify at-risk individuals among smokers and nonsmokers. However, the cost-effectiveness of this model has yet to be determined.

METHODS

The cost-effectiveness analysis simulates a Chinese birth cohort using a calibrated Markov model based on individual data from a prospective cohort of the Guangzhou Lung Cancer Screening Program. Health utility was extracted from the literature. Cost parameters were obtained from the price of basic medical services in public medical institutions. Our analysis evaluated 236 distinct screening strategies, varying by screening initiation age, risk thresholds, and smoking status. The primary outcomes were quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs).

RESULTS

For smokers, four strategies on the efficiency frontier yielded incremental QALYs ranging from 0.011 to 0.039 compared to no screening, with ICERs ranging from $21,874 to $55,038 when compared to the previous efficient strategies. The optimal strategy was annual screening of smokers aged 45 years and older with a 3-year risk of lung cancer incidence of 0.55%, offering the largest gain in QALYs at a willingness-to-pay (WTP) threshold of $38,224 (three times GDP per capita). This optimal strategy dominated the 2023 Chinese guideline-recommended strategy. For nonsmokers, the strategies on the efficiency frontier yielded incremental QALYs ranging from 0.006 to 0.041 compared to no screening, with ICERs ranging from $26,517 to $37,994 when compared to the previous efficient strategies. Correspondingly, the optimal strategy is annual screening of nonsmokers aged 45 years and older with a 3-year risk of lung cancer incidence of 0.20%.

CONCLUSIONS

This economic evaluation found that lung cancer screening strategies based on the Chinese NCC-LCm2021 model were cost-effective for both smokers and non-smokers in China. Furthermore, tailoring risk thresholds to smokers and nonsmokers can enhance the cost-effectiveness of lung cancer screening.

Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis

Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, with the aim of improving early detection and survival rates. However, no comprehensive review or meta-analysis has assessed the performance of these models across different sociocultural contexts. Therefore, this review systematically examines the performance of lung cancer risk prediction models in Western and Asian populations. PubMed and EMBASE were searched from inception through January 2023. Studies published in English that proposed a validated model on human populations with well-defined predictive performances were included. Two reviewers independently screened the titles and abstracts, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess study quality. A random-effects meta-analysis was performed, and a 95% confidence interval (CI) for model performance was reported. Between-study heterogeneity was adjusted for using the Hartung-Knapp-Sidik-Honkman test. A total of 54 studies were included, with 42 from Western countries and 12 from Asian countries. Most Western studies focused on ever-smokers (19/42; 45.2%) and the general population (17/42; 40.5%), and only two Asian studies developed models exclusively for never-smokers. Across both Western and Asian prediction models, the three most consistently included risk factors were age, sex, and family cancer history. In 45.2% (19/42) of Western and 50.0% (6/12) of Asian studies, models incorporated both traditional risk factors and biomarkers. In addition, 14.8% (8/54) of the studies directly compared biomarker-based models with those incorporating only traditional risk factors, demonstrating improved discrimination. Machine-learning algorithms were applied in eight Western models and two Asian models. External validation of PLCOM2012 (AUC = 0.748; 95% CI: 0.719-0.777) outperformed other prediction models, such as Bach (AUC = 0.710; 95% CI: 0.674-0.745) and Spitz models (AUC = 0.698; 95% CI: 0.640-0.755). Despite showing promising results, the majority of Asian risk models in our study lack external validation. Our review also highlights a significant gap in prediction models for never-smokers. Future research should focus on externally validating existing Asian models or incorporating relevant Asian risk factors into widely used Western models (PLCOM2012) to better account for unique risk profiles and lung cancer progression patterns in Asian populations.

Head-to-head comparisons of risk discrimination by questionnaire-based lung cancer risk prediction models: a systematic review and meta-analysis

Background

While different lung cancer risk prediction models have been established as essential tools to identify high-risk participants for lung cancer screening programs, evaluations of their risk discriminatory performances have reported heterogenous findings in different research cohorts. We therefore aimed to summarise results of head-to-head comparisons of the predictive performance of various lung cancer risk models performed within the same study population.

Methods

In this systematic review and meta-analysis, we performed a systematic search of PubMed and Web of Science databases for primary studies published from inception to Oct 16, 2024. Articles comparing the performance of questionnaire-based lung cancer risk models in an independent, external validation cohort of participants with previous or current smoking exposure were included. The main reasons for exclusion of studies were if only one model was assessed in the external population or risk discrimination was not evaluated. Random-effects meta-analyses were conducted to synthesize differences in the area under the curve (AUC) of two models compared in multiple populations. To assess the risk of bias, PROBAST (the Prediction model Risk of Bias Assessment Tool) was used. The study was registered with PROSPERO, CRD42023427911.

Findings

The systematic search yielded 5568 records. In total, 15 eligible studies were included in the meta-analysis, comprising 4,134,648 individuals with previous or current smoking exposure, of whom 45,448 (1.10%) developed LC within 5-7 years. Among the nine models that were compared, AUC differences reached up to 0.050 between two models. The Lung Cancer Risk Assessment Tool (LCRAT), Bach model and PLCOm2012 model consistently had a higher AUC when compared to any other model, with AUC differences ranging between 0.018 (95% CI 0.011, 0.026) and 0.044 (95% CI 0.038, 0.049). The risk of bias and applicability concerns were deemed low in eight, and high in seven of the included studies. Results excluding studies with high risk of bias were mostly consistent. Among eight of the 24 model pairs that were compared, there was notable between-study heterogeneity (I2 ≥50%).

Interpretation

Our systematic review and meta-analyses of head-to-head comparisons disclose major differences in predictive performance of widely used lung cancer risk models. Although our review is limited to the availability of head-to-head comparisons, evidence from current cohort-based model comparisons indicates that the LCRAT, Bach and PLCOm2012 consistently outperformed alternative questionnaire-based risk prediction tools.

Funding

Funded by the European Union.

Exploratory evidence maps for the WHO Classification of Tumours 5th edition for lung and thymus tumors

The WHO Classification of Tumours (WCT) guides cancer diagnosis, treatment, and research. However, research evidence in pathology continuously changes, and new evidence emerges. Correct assessment of evidence in the WCT 5th edition (WCT-5) and identification of high level of evidence (LOE) studies based on study design are needed to improve future editions. We aimed at producing exploratory evidence maps for WCT-5 Thoracic Tumours, specifically lung and thymus tumors. We extracted citations from WCT-5, and imported and coded them in EPPI-Reviewer. The maps were plotted using EPPI-Mapper. Maps displayed tumor types (columns), descriptors (rows), and LOE (bubbles using a four-color code). We included 1434 studies addressing 51 lung, and 677 studies addressing 25 thymus tumor types from WCT-5 thoracic tumours volume. Overall, 87.7% (n = 1257) and 80.8% (n = 547) references were low, and 4.1% (n = 59) and 2.2% (n = 15) high LOE for lung and thymus tumors, respectively. Invasive non-mucinous adenocarcinoma of the lung (n = 215; 15.0%) and squamous cell carcinoma of the thymus (n = 93; 13.7%) presented the highest number of references. High LOE was observed for colloid adenocarcinoma of the lung (n = 11; 18.2%) and type AB thymoma (n = 4; 1.4%). Tumor descriptors with the highest number of citations were prognosis and prediction (n = 273; 19.0%) for lung, and epidemiology (n = 186; 28.0%) for thymus tumors. LOE was generally low for lung and thymus tumors. This study represents an initial step in the WCT Evidence Gap Map (WCT-EVI-MAP) project for mapping references in WCT-5 for all tumor types to inform future WCT editions.

Polycyclic aromatic hydrocarbon and its adducts in peripheral blood: Gene and environment interaction among Chinese population

BACKGROUND

Benzo(a)pyrene (B[a]P) is the most widely concerned polycyclic aromatic hydrocarbons (PAHs), which metabolizes benzo(a)pyrene-7,8-dihydrodiol-9,10-epoxide (BPDE) in vivo to produce carcinogenic effect on the body. Currently, there is limited research on the role of the variation of metabolic enzymes in this process.

METHODS

We carried out a study including 752 participants, measured the concentrations of 16 kinds PAHs in both particle and gaseous phases, urinary PAHs metabolites, leukocyte BPDE-DNA adduct and serum BPDE- Albumin (BPDE-Alb) adduct, and calculated daily intake dose (DID) to assess the cumulative exposure of PAHs. We conducted single nucleotide polymorphism sites (SNPs) of metabolic enzymes, explored the exposure-response relationship between the levels of exposure and BPDE adducts using multiple linear regression models.

RESULT

Our results indicated that an interquartile range (IQR) increase in B[a]P, PAHs, BaPeq, 1-hydroxypyrene (1-OHP), 1-hydroxynaphthalene (1-OHNap) and 2-hydroxynaphthalene (2-OHNap) were associated with 26.53 %, 24.24 %, 28.15 %, 39.15 %, 12.85 % and 14.09 % increase in leukocyte BPDE-DNA adduct (all P < 0.05). However, there was no significant correlation between exposure with serum BPDE-Alb adduct (P > 0.05). Besides, we also found the polymorphism of CYP1A1(Gly45Asp), CYP2C9 (Ile359Leu), and UGT1A1(downstream) may affect BPDE adducts level.

CONCLUSION

Our results indicated that leukocyte BPDE-DNA adduct could better reflect the exposure to PAHs. Furthermore, the polymorphism of CYP1A1, CYP2C9 and UGT1A1affected the content of BPDE adducts.