Brief Report: Validation of the Sybil Lung Cancer Risk Prediction Model in a Predominantly Black Screening Cohort.

INTRODUCTION: Lung cancer remains the leading cause of cancer-related mortality in the United States and worldwide. Black Americans have higher lung cancer incidence and mortality risk despite lower cumulative tobacco exposure. Sybil is an open-source deep learning model that predicts future lung cancer risk from a single LDCT. Sybil was trained using National Lung Screening Trial data in which more than 90% of participants were White; and Sybil's performance in diverse populations remains unclear. METHODS: We conducted a retrospective cohort study of 5,883 LDCTs from 3,177 participants (2014-2025). Sybil generated risk predictions for 1-6 years. Model performance was assessed using AUC. RESULTS: This cohort included 64.9% Black individuals. Among 105 lung cancers, 61% were diagnosed within 1 year. Sybil demonstrated strong discrimination for baseline LDCTs, with AUCs ranging from 0.91 at 1 year (95% CI, 87.7-94.6) to 0.82 at 6 years (95% CI, 77.6-86.1), with comparable performance between Black and non-Black individuals. CONCLUSIONS: Sybil demonstrated robust lung cancer risk prediction in this screening cohort, supporting its generalizability and highlighting its potential to improve risk-based screening beyond current eligibility criteria in diverse populations.