Sybil AI Model Shows Strong Lung Cancer Risk Prediction on Low-Dose CT

Open-source Sybil AI detects lung cancer risk effectively in Asian heavy smokers on low-dose CT.

Key Details

• Sybil is an open-source deep learning model that uses low-dose CT (LDCT) to predict lung cancer risk.
• Study included 18,057 Asian patients (with at least one follow-up scan) from 2004–2021.
• Sybil achieved overall AUC of 0.91 (1-year) and 0.74 (6-year); heavy smoking subgroup AUCs were 0.94 (1-year, visible cancers) and 0.7 (6-year, future cancers).
• Performance was weaker (AUC 0.56 for 6-year, future cancers) in never/light-smoking subgroup.
• The model may help optimize follow-up intervals in lung cancer screening programs.
• External validation is needed before widespread adoption.

Why It Matters

This research demonstrates promising performance of an AI tool for individualized lung cancer risk estimation using routine LDCT in underrepresented Asian populations, potentially supporting more tailored screening protocols. Reliable, open-source risk assessment models like Sybil may improve early detection and resource allocation in lung cancer care.

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