Low-dose CT scan screening for lung cancer: To establish an accurate predictive model through radiomics methods and optimize follow-up CT screening strategies
Principal Investigator
Name
Yin Cai
Degrees
Ph.D.
Institution
The Hong Kong Polytechnic University
Position Title
Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-863
Initial CDAS Request Approval
Dec 30, 2021
Title
Low-dose CT scan screening for lung cancer: To establish an accurate predictive model through radiomics methods and optimize follow-up CT screening strategies
Summary
The popularity of low-dose CT scans has increased the early detection rate of lung cancer, while small nodules have also been diagnosed early. The positive detection rate is high but the number of diagnosed lung cancers is very low. Pulmonary nodules can lead to a large number of false positives or necessitate a general increase in the number of CT scans obtained in lung cancer screening subjects. Illustrated by the recent recommendation of the US Preventive Services Task Force to obtain CT in high-risk subjects yearly, appropriate nodule management will become increasingly relevant . So based on low-dose CT images, we will establish and verify an accurate lung cancer prediction model by radiomics features for high-risk groups.
Aims
1.Developing strategies for long term follow up of suspicious lung cancer patient, avoid excessive medical treatment and reduce medical costs
2.Reduce false positive screening results, Reduce the minimum radiation dose for lung cancer screening
3.Establishing a risk prediction model for lung cancer of high-risk groups
Collaborators
Xinzhi Teng, The Hong Kong Polytechnic University
Jiang Zhang, The Hong Kong Polytechnic University
Xinyang Han, The Hong Kong Polytechnic University