External Validation of Lung Cancer Prediction Models Combining Epidemiological Predictors in Chinese Ever and Never Smokers: Guangzhou Biobank Cohort Study
ABSTRACT Objective This study aimed to externally validate existing lung cancer models using data from the Guangzhou Biobank Cohort Study (GBCS) and compare their predictive performance for Chinese ever and never smokers. Methods We evaluated the discrimination and calibration of LCRAT (Lung Cancer...
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.71104 |
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| author | Bo Xing Feng Xin Yue Pan Jing Ru Huang Chao Qiang Jiang Wei Sen Zhang Feng Zhu Jing Pan Tai Hing Lam |
| author_facet | Bo Xing Feng Xin Yue Pan Jing Ru Huang Chao Qiang Jiang Wei Sen Zhang Feng Zhu Jing Pan Tai Hing Lam |
| author_sort | Bo Xing Feng |
| collection | DOAJ |
| description | ABSTRACT Objective This study aimed to externally validate existing lung cancer models using data from the Guangzhou Biobank Cohort Study (GBCS) and compare their predictive performance for Chinese ever and never smokers. Methods We evaluated the discrimination and calibration of LCRAT (Lung Cancer Risk Assessment Tool), LLP version 2 (Liverpool Lung Project version 2), LLP version 3 (Liverpool Lung Project version 3), HUNT (HUNT was derived from the Nord‐Trøndelag Health Study), OWL (Optimized Early Warning Model for Lung Cancer Risk), LCRS (Lung Cancer Risk Score), PLCOm2012 (Prostate, Lung, Colorectal, and Ovarian 2012 model), PLCOall2014 (Prostate, Lung, Colorectal, and Ovarian 2014 model), NHIS (Korean National Health Insurance Service), LLPi (Liverpool Lung Project Risk Prediction Model for Lung Cancer Incidence), Pittsburgh, and Bach models. We compared the performance of models and Chinese lung cancer screening (T/CPMA 013‐2020), US Preventive Services Task Force 2021 (USPSTF‐2021) and Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) criteria. Results The LLP version 2, LLP version 3, OWL, LCRS, PLCOall2014, and LLPi models showed better performance in ever smokers than in never smokers, with higher AUC (0.72–0.82 vs. 0.69–0.71) and E/O (expected to observed) ratios (0.57–0.79 vs. 0.60–0.69), while the LCRAT, HUNT, PLCOm2012, NHIS, Pittsburgh, and Bach models showed good performance in ever smokers, with AUC ranging from 0.70 to 0.79 and E/O ratios from 0.57 to 0.75. The T/CPMA 013‐2020, USPSTF‐2021, and NELSON criteria identified 56.52%–75.58% of high‐risk individuals at 5, 6, 6.6, 8.7, and 10 years, while the LCRAT, LLP version 2, LLP version 3, HUNT, OWL, LCRS, PLCOm2012, PLCOall2014, NHIS, LLPi, Pittsburgh, and Bach models identified 70.70%–89.72% of high‐risk individuals. Conclusions Most lung cancer risk prediction models showed good performance and identified more cases than screening criteria. Replacing screening criteria with risk prediction models may increase lung cancer screening efficiency. |
| format | Article |
| id | doaj-art-3d55a9d79d7d447ab381e2b72c8d953a |
| institution | DOAJ |
| issn | 2045-7634 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-3d55a9d79d7d447ab381e2b72c8d953a2025-08-20T03:06:40ZengWileyCancer Medicine2045-76342025-08-011415n/an/a10.1002/cam4.71104External Validation of Lung Cancer Prediction Models Combining Epidemiological Predictors in Chinese Ever and Never Smokers: Guangzhou Biobank Cohort StudyBo Xing Feng0Xin Yue Pan1Jing Ru Huang2Chao Qiang Jiang3Wei Sen Zhang4Feng Zhu5Jing Pan6Tai Hing Lam7School of Nursing Guangzhou Medical University Guangzhou Guangdong ChinaSchool of Public Health Guangzhou Medical University Guangzhou Guangdong ChinaSchool of Nursing Guangzhou Medical University Guangzhou Guangdong ChinaGuangzhou NO. 12 Hospital Guangzhou Guangdong ChinaGuangzhou NO. 12 Hospital Guangzhou Guangdong ChinaGuangzhou NO. 12 Hospital Guangzhou Guangdong ChinaGuangzhou NO. 12 Hospital Guangzhou Guangdong ChinaSchool of Public Health The University of Hong Kong Hong Kong ChinaABSTRACT Objective This study aimed to externally validate existing lung cancer models using data from the Guangzhou Biobank Cohort Study (GBCS) and compare their predictive performance for Chinese ever and never smokers. Methods We evaluated the discrimination and calibration of LCRAT (Lung Cancer Risk Assessment Tool), LLP version 2 (Liverpool Lung Project version 2), LLP version 3 (Liverpool Lung Project version 3), HUNT (HUNT was derived from the Nord‐Trøndelag Health Study), OWL (Optimized Early Warning Model for Lung Cancer Risk), LCRS (Lung Cancer Risk Score), PLCOm2012 (Prostate, Lung, Colorectal, and Ovarian 2012 model), PLCOall2014 (Prostate, Lung, Colorectal, and Ovarian 2014 model), NHIS (Korean National Health Insurance Service), LLPi (Liverpool Lung Project Risk Prediction Model for Lung Cancer Incidence), Pittsburgh, and Bach models. We compared the performance of models and Chinese lung cancer screening (T/CPMA 013‐2020), US Preventive Services Task Force 2021 (USPSTF‐2021) and Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) criteria. Results The LLP version 2, LLP version 3, OWL, LCRS, PLCOall2014, and LLPi models showed better performance in ever smokers than in never smokers, with higher AUC (0.72–0.82 vs. 0.69–0.71) and E/O (expected to observed) ratios (0.57–0.79 vs. 0.60–0.69), while the LCRAT, HUNT, PLCOm2012, NHIS, Pittsburgh, and Bach models showed good performance in ever smokers, with AUC ranging from 0.70 to 0.79 and E/O ratios from 0.57 to 0.75. The T/CPMA 013‐2020, USPSTF‐2021, and NELSON criteria identified 56.52%–75.58% of high‐risk individuals at 5, 6, 6.6, 8.7, and 10 years, while the LCRAT, LLP version 2, LLP version 3, HUNT, OWL, LCRS, PLCOm2012, PLCOall2014, NHIS, LLPi, Pittsburgh, and Bach models identified 70.70%–89.72% of high‐risk individuals. Conclusions Most lung cancer risk prediction models showed good performance and identified more cases than screening criteria. Replacing screening criteria with risk prediction models may increase lung cancer screening efficiency.https://doi.org/10.1002/cam4.71104external validationlung cancermodel performancerisk prediction |
| spellingShingle | Bo Xing Feng Xin Yue Pan Jing Ru Huang Chao Qiang Jiang Wei Sen Zhang Feng Zhu Jing Pan Tai Hing Lam External Validation of Lung Cancer Prediction Models Combining Epidemiological Predictors in Chinese Ever and Never Smokers: Guangzhou Biobank Cohort Study Cancer Medicine external validation lung cancer model performance risk prediction |
| title | External Validation of Lung Cancer Prediction Models Combining Epidemiological Predictors in Chinese Ever and Never Smokers: Guangzhou Biobank Cohort Study |
| title_full | External Validation of Lung Cancer Prediction Models Combining Epidemiological Predictors in Chinese Ever and Never Smokers: Guangzhou Biobank Cohort Study |
| title_fullStr | External Validation of Lung Cancer Prediction Models Combining Epidemiological Predictors in Chinese Ever and Never Smokers: Guangzhou Biobank Cohort Study |
| title_full_unstemmed | External Validation of Lung Cancer Prediction Models Combining Epidemiological Predictors in Chinese Ever and Never Smokers: Guangzhou Biobank Cohort Study |
| title_short | External Validation of Lung Cancer Prediction Models Combining Epidemiological Predictors in Chinese Ever and Never Smokers: Guangzhou Biobank Cohort Study |
| title_sort | external validation of lung cancer prediction models combining epidemiological predictors in chinese ever and never smokers guangzhou biobank cohort study |
| topic | external validation lung cancer model performance risk prediction |
| url | https://doi.org/10.1002/cam4.71104 |
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