2023 World Conference on Lung Cancer (Posters)-P1.14. Validation of Lung Cancer Risk Prediction Models in an Asbestos Exposed Population

P1.14. Validation of Lung Cancer Risk Prediction Models in an Asbestos Exposed Population - PDF(Slides)

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This study aimed to validate lung cancer risk prediction models and eligibility criteria for population-level lung cancer screening in an asbestos-exposed population. The participants in the study underwent screening with low-dose CT over a 10-year period. The majority of participants were men and had a history of smoking.<br /><br />The discriminatory performance of the lung cancer risk models was compared. The LLPv2 risk model had better discriminative performance than the PLCO2012 and PLCO2014 models. The sensitivities ranged from 0.5 for PLCO2014 to 0.94 for LLPv2. However, LLPv2 had poorer specificity compared to the PLCO variants. The USPSTF2021 criteria had lower sensitivity, while the MSAC had the weakest performance.<br /><br />Calibration analysis showed that all PLCO models underestimated risk in the asbestos-exposed population, while the LLPv2 model overestimated the lung cancer risk. This assessment was confirmed by the MADCAP curves.<br /><br />In conclusion, this study suggests that bespoke risk prediction models specifically developed and calibrated for individuals exposed to asbestos are needed for more effective identification of those who will benefit from lung cancer screening. The current risk models and eligibility criteria do not adequately account for occupational risk exposures such as asbestos, leading to underestimation and inaccurate risk assessment. By developing customized models, it may be possible to improve the accuracy of risk assessment and better identify high-risk individuals for lung cancer screening.

Asset Subtitle

Chellan Kumarasamy

Meta Tag

Speaker Chellan Kumarasamy

Topic Screening & Early Detection: Implementation

Keywords

lung cancer risk prediction models

eligibility criteria

population-level lung cancer screening

asbestos-exposed population

low-dose CT

discriminatory performance

LLPv2 risk model

PLCO2012 model

sensitivities

specificity