A new study validates the Sybil AI model for predicting lung cancer risk using low-dose CT in a predominantly Black cohort at an urban safety-net hospital.
Key Details
- 1Study presented at the 2025 IASLC World Conference on Lung Cancer.
- 2Validation conducted at UI Health on 2,092 baseline low-dose CT scans from 2014-2024.
- 3Cohort primarily Non-Hispanic Black (62%), with 13% Hispanic and 4% Asian.
- 4Sybil demonstrated strong Area Under the Curve (AUC): 0.94 (1 yr), 0.90 (2 yr), 0.86 (3 yr), 0.85 (4 yr), 0.80 (5 yr), 0.79 (6 yr).
- 5Results remained strong after restricting to Black participants and excluding recent diagnoses.
- 6The Sybil Implementation Consortium will advance to prospective clinical trials for clinical workflow integration.
Why It Matters
Demonstrating reliable AI performance in racially and socioeconomically diverse settings is crucial for equitable lung cancer screening. This validation may facilitate broader adoption and help address longstanding disparities in lung cancer outcomes.
Source
EurekAlert
Related News
•EurekAlert
Advancements in CRC Screening: Imaging, AI, and Point-of-Care Diagnostics
Recent innovations in colorectal cancer screening include advanced imaging, AI tools, and novel diagnostics to improve early detection and outcomes.
•EurekAlert
AI Voice Analysis Shows Promise for Early Laryngeal Cancer Detection
Researchers demonstrated AI could detect early laryngeal cancer from voice recordings, distinguishing it from benign conditions.
•EurekAlert
BU Researchers Develop AI to Predict Alzheimer's Markers from Brain Scans
Boston University scientists developed an AI model that predicts Alzheimer's-related protein levels using brain imaging and clinical data.