Journal

Thorax

Papers (3)

Lung cancer risk assessment by prediction model: a global perspective

Background Numerous lung cancer risk prediction models have been developed and validated worldwide. It is imperative to offer a comprehensive overview and comparative analysis of their performances. Methods We conducted an extensive literature search to identify studies developing and/or validating lung cancer risk prediction models. Then we summarised and compared the external performance of these models, focusing on discriminative accuracy (C-index) and calibration performance (E:O ratio). Results After an initial screening of 10 210 articles, 35 studies on 21 distinct prediction models were identified, which used 42 different types of predictors spanning seven categories. Notable performance variations were observed in external validations. In North American cohorts, the C-index ranged from 0.60 to 0.87, with E:O ratios from 0.62 to 3.70. Among the European cohorts, the Trøndelag health study HUNT and CanPredict exhibited C-indices surpassing 0.870. Conversely, the Bach, lung cancer risk assessment tool (LCRAT), prostate, lung, colorectal and ovarian cancer screening (PLCO) m2012 and PLCO all2014 performed poorly in electronic health records of the Qresearch database subgroup, with C-indices falling below 0.60. PLCO m2012 reached the best E:O ratio of 1.00 (95% CI: 0.93 to 1.08) in the UK Biobank subgroup. In Asian cohorts, the C-index ranged from 0.54 to 0.87. Only three models, Korean Men, LCRAT and Liverpool lung project incidence risk model (LLPi), achieved a C-index exceeding 0.80. LCRAT demonstrated the best calibration, while Hoggart performed the worst. Conclusions Performance of lung cancer risk prediction models, despite being well developed and validated, varies in diverse populations. Significant regional imbalance persists in the development of these models. Rigorous external validation or recalibration study in the target population is crucial in accordance with the guidance prior to model implementation. PROSPERO registration number CRD42022324602.

Association between ultra-processed food consumption and lung cancer risk: a population-based cohort study

Background The evidence on associations between ultra-processed foods (UPF) and lung cancer risk is limited and inconsistent. Research question Are UPF associated with an increased risk of lung cancer, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC)? Methods Data of participants in this study were collected from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Dietary intakes were assessed through a validated diet history questionnaire. These foods were categorised using the NOVA classification according to the degree of processing in the PLCO Cancer Screening Cohort. All cases of incident lung cancer were pathologically verified. Multivariable Cox regression was used to assess the association between consumption of UPF and lung cancer after adjustment for various potential confounders, including key risk factors related to lung cancer and overall diet quality. Results A total of 1706 cases of lung cancer cases, including 1473 NSCLC and 233 SCLC, were identified during a mean follow-up of 12.2 years among 101 732 adults (mean age 62.5 years). After multivariable adjustments, individuals in the highest quarters for UPF consumption had a higher risk of lung cancer (HR=1.41, 95% CI 1.22 to 1.60), NSCLC (HR=1.37, 95% CI 1.20 to 1.58) and SCLC (HR=1.44, 95% CI 1.03 to 2.10) compared with those in the lowest quarter. These results remained statistically significant after a large range of subgroup and sensitivity analyses. Conclusions Higher consumption of UPF is associated with an increased risk of lung cancer, NSCLC and SCLC. Although additional research in other populations and settings is warranted, these findings suggest the healthy benefits of limiting UPF.

Publisher

BMJ

ISSN

0040-6376

Thorax