Evaluation and Optimization of Lung Cancer Screening Effectiveness Using the NLST Dataset
Principal Investigator
Name
Ted Li
Degrees
M.P.H.
Institution
China Changzhou Lunghealth Medical Equipment Co., Ltd.
Position Title
Senior Software Engineer
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1123
Initial CDAS Request Approval
Sep 18, 2023
Title
Evaluation and Optimization of Lung Cancer Screening Effectiveness Using the NLST Dataset
Summary
This study aims to utilize the NLST (National Lung Screening Trial) dataset to investigate the effectiveness of lung cancer screening protocols and propose optimization strategies to enhance the accuracy and efficiency of early lung cancer diagnosis. Lung cancer, as one of the leading causes of mortality worldwide, emphasizes the critical importance of early detection for patient survival. The NLST dataset represents a valuable resource from a large-scale clinical trial focused on lung cancer screening, containing medical information and screening results of thousands of participants.
Aims
Data Analysis and Modeling: Leveraging the NLST dataset, we will conduct in-depth data analysis to assess the performance of different screening protocols, including low-dose CT scans and conventional X-rays. Machine learning and statistical models will be constructed to evaluate the accuracy and sensitivity of these screening methods.
Screening Optimization: Based on the analysis results, we will provide recommendations for optimizing lung cancer screening protocols to improve early diagnosis outcomes. This may involve adjustments to screening frequency, participant selection criteria, and enhancements to the diagnostic workflow.
Collaborators
None