Prediction of lung cancer brain metastasis risk with low-dose CT
Principal Investigator
Name
Bangxia Suo
Degrees
M.S.
Institution
Renmin University of China
Position Title
Student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-937
Initial CDAS Request Approval
Jul 22, 2022
Title
Prediction of lung cancer brain metastasis risk with low-dose CT
Summary
We realized that some therapies, like icotinib, Imatinib and T-DM1, have weak ability to cross the blood-brain-barrier; and some therapies, like pembrolizumab, have variable central nervous system penetration. Meaning while, some cancer patients may have lower risk of brain metastasis. Thus, we hypothesis that the therapies that I mentioned above may be more suitable for the subgroup of cancer patients associated with lower risk of brain metastasis and achieve a longer period of mPFS and mOS.
To achive this goal of screening cancer patients with lower risk of brain metastasis, we propose to build a deep learning based algorithm to help the procedure. We may use clinical factors, CT or MRI images with follow-up data of brain metastasis event observations as input to train and validate the deep learning model, and then use the model to do the prediction.
To achive this goal of screening cancer patients with lower risk of brain metastasis, we propose to build a deep learning based algorithm to help the procedure. We may use clinical factors, CT or MRI images with follow-up data of brain metastasis event observations as input to train and validate the deep learning model, and then use the model to do the prediction.
Aims
the aims of the project is to build the deep learning model
Collaborators
no collaborator