Construction and evaluation of an intelligent diagnosis model for lung cancer based on multi-round low-dose CT images
Principal Investigator
Name
Qianyun Jin
Degrees
M.D.
Institution
Tianjin medical University Cancer Institute and Hospital
Position Title
student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1296
Initial CDAS Request Approval
Jul 22, 2024
Title
Construction and evaluation of an intelligent diagnosis model for lung cancer based on multi-round low-dose CT images
Summary
Studies have shown that LDCT screening in high-risk individuals reduces lung cancer mortality by 20% compared with chest X-ray screening. With the rapid development of deep learning technology, the convolutional neural network deep learning model has achieved good results in the field of medical image recognition.The intelligent diagnostic model has been applied to single-round LDCT to detect lung cancer, but there is no research to explore whether multi-round LDCT intelligent diagnostic model can improve the effect of lung cancer screening, this study will further explore the construction of multi-round LDCT intelligent diagnostic model, and study the association between LDCT progression and lung cancer morbidity and mortality based on this model. In this study, the intelligent diagnosis model is applied to the interpretation of multi-round LDCT to further optimize the effect of lung cancer examination.
Aims
1) To evaluate the construction of multi-round LDCT intelligent diagnostic lung cancer model.
2) To assess the effect of multi-round LDCT intelligent diagnosic lung cancer model, compared with the judgment of doctors.
3) To evaluate the association of LDCT progression with lung cancer incidence and mortality based on this intelligent diagnostic model.
Collaborators
None.