Sybil lung cancer risk model shows high accuracy in Black-predominant screening cohort

Sybil, a deep learning model using a single low-dose CT, predicted future lung cancer with area under the curve (AUC) 0.91 at 1 year (95% CI 87.7-94.6) and 0.82 at 6 years, with similar performance in Black and non-Black individuals in 5,883 LDCTs (64.9% Black).

• Among 105 lung cancers in the cohort, 61% were diagnosed within 1 year of the index low-dose CT.
• This validation cohort was external to the original National Lung Screening Trial training data and included 64.9% Black participants.

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