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The Practical Application of the Lung Cancer Risk Prediction Model 'Sybil': Replicating and Validating Study Results in Taiwan

Principal Investigator
Name
Tsung-Ren Huang
Degrees
Ph.D.
Institution
National Taiwan University
Position Title
Associate Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-1186
Initial CDAS Request Approval
Jan 11, 2024
Title
The Practical Application of the Lung Cancer Risk Prediction Model 'Sybil': Replicating and Validating Study Results in Taiwan
Summary
Our intention is to replicate and validate the findings of the paper titled "Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography." This study demonstrated promising results, which prompts our interest in applying this method to the NLST dataset and beyond. Our ultimate goal is to reproduce the model outlined in the paper and adapt it for real-world uses in Taiwan.
Aims

1.Reproduce the Sybil model using the NLST dataset to replicate its findings.
2. Implement the Sybil model in practical healthcare settings within Taiwan.

Collaborators

Tsung-Ren Huang, National Taiwan University
Chun-Rong Huang, National Cheng Kung University