Risk models may improve lung cancer screening

Analysis found no single prediction model optimized screening eligibility, sensitivity, and efficiency across all racial and ethnic groups.

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Risk-based lung cancer screening strategies may enhance efficiency and reduce disparities compared to current USPSTF eligibility criteria.
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The study evaluated 16 lung cancer risk prediction models among over 641,000 US adults aged 50 to 80 with a smoking history.
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Existing prediction models showed significant underestimation of lung cancer risk in non-Hispanic Black participants.
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Models incorporating race and ethnicity as predictors generally demonstrated better calibration across racial and ethnic groups.
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The study highlighted the need for further optimization of prediction models to address disparities in lung cancer screening.

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