Deep learning based lymph node metastasis and prognosis prediction for lung cancer
Principal Investigator
Name
Danqing Hu
Degrees
Ph.D
Institution
Zhejiang lab
Position Title
Assistant Researcher
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-939
Initial CDAS Request Approval
Jul 22, 2022
Title
Deep learning based lymph node metastasis and prognosis prediction for lung cancer
Summary
Lung cancer is the leading cause of cancer death worldwide. Lymph node metastasis is critical for treatment decision-making, but difficult to be accurately identified pre-operatively. In our project, we first aim to develop novel deep learning method using pre-operative clinical data, sequential CT images, and biopsy pathology slides to predict lymph node metastasis to help clinicians' decision-making. Besides, for post-operative patients, the risk of recurrence and death is also critical to the patient's treatment. So we also plan to employ pre-operative and post-operative multi-modal data including clinical data, follow-up data, CT images, and pathology slide images, to develop survival and recurrence prediction models for lung cancer patients. NLST includes clinical data, follow-up data, CT images, and pathology slides, which is necessary for our research. We will use the NLST data to train and validate the proposed methods.
Aims
1. Developing and validating lymph node metastasis prediction models using pre-operative clinical, CT image, and biopsy pathology multi-modal data.
2. Developing and validating recurrence and survival prediction models using multi-modal data including clinical, follow-up, CT, and pathology data.
Collaborators
Lechao Cheng, Zhejiang lab
Zhengxing Huang, Zhejiang University