Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2026
Brief Report: Validation of the Sybil Lung Cancer Risk Prediction Model in a Predominantly Black Screening Cohort.
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Pasquinelli Mary M, Durney Clinton H, Hill Teri, Atueyi Ukamaka, Kovitz Kevin, Abdelhady Khaled, Koshy Matthew, Wang Meida, Osarogiagbon Raymond U, Mayfield William R, Warner Erica T, Smeltzer Matthew P, Ogbeide Osa, Feldman Jill, Potter Alexandra L, Jeffrey Yang Chi-Fu, Kent Denise A, Chang Allison, Fintelmann Florian J, Sequist Lecia V, Weinberg Frank
Аннотация
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.
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.