Risk prediction model base on low-dose computed tomography screening programme in prevention of lung cancer in China and US
Principal Investigator
Name
Sipeng Shen
Institution
Nanjing Medical University
Position Title
Assistant Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-755
Initial CDAS Request Approval
Jan 31, 2021
Title
Risk prediction model base on low-dose computed tomography screening programme in prevention of lung cancer in China and US
Summary
Lung cancer screening with low-dose computed tomography (LDCT) has been widely implemented in many countries. We thus conducted this multiple center population-based cohort study in the context of the Cancer Screening Program in Urban China (CanSPUC) and NLST. Lung cancer risk prediction models will be developed base on demographics (age, sex. BMI, smoking, family histology) and LDCT results (lung node size, et. al.). Further, we will compare the model performance between US and China.
Aims
We aim to develop a lung cancer risk prediction model and compare the model performance between US and China.
Collaborators
None
Related Publications
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OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations.
Pan Z, Zhang R, Shen S, Lin Y, Zhang L, Wang X, Ye Q, Wang X, Chen J, Zhao Y, Christiani DC, Li Y, Chen F, Wei Y
EBioMedicine. 2023 Jan 24; Volume 88: Pages 104443 PUBMED