Study on multimodal diagnosis and prognosis model of lung cancer
Principal Investigator
Name
Wenmiao Wang
Degrees
M.D.s
Institution
Research Center for Intelligent Medical Information Processing, Shandong University
Position Title
Scientific researcher
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1104
Initial CDAS Request Approval
Aug 8, 2023
Title
Study on multimodal diagnosis and prognosis model of lung cancer
Summary
Currently, predictive biomarkers for response to immune checkpoint inhibitor (ICI) therapy in lung cancer are limited. Identifying such biomarkers would be useful to refine patient selection and guide precision therapy. To develop a machine-learning (ML)-based tumor-infiltrating lymphocytes (TILs) scoring approach, and to evaluate TIL association with clinical outcomes in patients with advanced non–small cell lung cancer (NSCLC).Objective response rate (ORR), progression-free survival(PFS), and overall survival (OS) were determined by blinded medical record review. The area
under curve (AUC) of TIL levels, TMB, and PD-L1 in predicting ICI response were calculated using ORR.
under curve (AUC) of TIL levels, TMB, and PD-L1 in predicting ICI response were calculated using ORR.
Aims
1.TILs and Clinical Outcomes
2.Relative Significance of Different Biomarkers in the Association With ICI Response
3.Prediction of ICI Response in PD-L1 Subgroups
Collaborators
Xiaogang zhao Department of Thoracic Surgery, The Second Hospital of Shandong University, Jinan, Shandong, China. Electronic address: zhaoxiaogang@sdu.edu.cn.
Zhi Liu School of Information Science and Engineering, Shandong University, Qingdao, Shandong, China. Electronic address: liuzhi@sdu.edu.cn.