Xiaoshuang Feng PhD , Patrick Goodley MBBCh , Karine Alcala MS , Florence Guida PhD , Prof Rudolf Kaaks PhD , Prof Roel Vermeulen PhD , George S Downward PhD , Catalina Bonet MSc , Sandra M Colorado-Yohar PhD , Demetrius Albanes MD , Stephanie J Weinstein PhD , Prof Marcel Goldberg PhD , Prof Marie Zins PhD , Prof Caroline Relton PhD , Prof Arnulf Langhammer PhD , Anne Heidi Skogholt PhD , Mattias Johansson PhD , Hilary A Robbins PhD
{"title":"评估风险预测模型以选择欧洲肺癌筛查参与者:前瞻性队列联合分析","authors":"Xiaoshuang Feng PhD , Patrick Goodley MBBCh , Karine Alcala MS , Florence Guida PhD , Prof Rudolf Kaaks PhD , Prof Roel Vermeulen PhD , George S Downward PhD , Catalina Bonet MSc , Sandra M Colorado-Yohar PhD , Demetrius Albanes MD , Stephanie J Weinstein PhD , Prof Marcel Goldberg PhD , Prof Marie Zins PhD , Prof Caroline Relton PhD , Prof Arnulf Langhammer PhD , Anne Heidi Skogholt PhD , Mattias Johansson PhD , Hilary A Robbins PhD","doi":"10.1016/S2589-7500(24)00123-7","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts.</p></div><div><h3>Methods</h3><p>We analysed 240 137 participants aged 45–80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCO<sub>m2012</sub>), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London—Death (UCLD), the University College London—Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) criteria.</p></div><div><h3>Findings</h3><p>Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCO<sub>m2012</sub>, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59–0·77) to 0·83 (0·78–0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57–0·72) to 0·78 (0·74–0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a population of equal size to USPSTF-2021, the PLCO<sub>m2012</sub>, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI, models identified 77·6%–79·1% of future cases, although they selected slightly older individuals compared with USPSTF-2021 criteria. Results were similar for USPSTF-2013 and NELSON.</p></div><div><h3>Interpretation</h3><p>Several lung cancer risk prediction models showed good performance in European countries and might improve the efficiency of lung cancer screening if used in place of categorical eligibility criteria.</p></div><div><h3>Funding</h3><p>US National Cancer Institute, l’Institut National du Cancer, Cancer Research UK.</p></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 9","pages":"Pages e614-e624"},"PeriodicalIF":23.8000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589750024001237/pdfft?md5=f39b6b82aa1c03f5fab734e0e84a6e1f&pid=1-s2.0-S2589750024001237-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis\",\"authors\":\"Xiaoshuang Feng PhD , Patrick Goodley MBBCh , Karine Alcala MS , Florence Guida PhD , Prof Rudolf Kaaks PhD , Prof Roel Vermeulen PhD , George S Downward PhD , Catalina Bonet MSc , Sandra M Colorado-Yohar PhD , Demetrius Albanes MD , Stephanie J Weinstein PhD , Prof Marcel Goldberg PhD , Prof Marie Zins PhD , Prof Caroline Relton PhD , Prof Arnulf Langhammer PhD , Anne Heidi Skogholt PhD , Mattias Johansson PhD , Hilary A Robbins PhD\",\"doi\":\"10.1016/S2589-7500(24)00123-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. 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Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis
Background
Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts.
Methods
We analysed 240 137 participants aged 45–80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCOm2012), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London—Death (UCLD), the University College London—Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) criteria.
Findings
Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59–0·77) to 0·83 (0·78–0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57–0·72) to 0·78 (0·74–0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a population of equal size to USPSTF-2021, the PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI, models identified 77·6%–79·1% of future cases, although they selected slightly older individuals compared with USPSTF-2021 criteria. Results were similar for USPSTF-2013 and NELSON.
Interpretation
Several lung cancer risk prediction models showed good performance in European countries and might improve the efficiency of lung cancer screening if used in place of categorical eligibility criteria.
Funding
US National Cancer Institute, l’Institut National du Cancer, Cancer Research UK.
期刊介绍:
The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health.
The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health.
We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.