ArticleCancer medicine2025
External Validation of Lung Cancer Prediction Models Combining Epidemiological Predictors in Chinese Ever and Never Smokers: Guangzhou Biobank Cohort Study.
Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
objectiveThis 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.
methodsWe 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.
resultsThe 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.
conclusionsMost 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.