CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study

Here, we used pre-treatment CT images to develop and evaluate a radiomic signature that can predict the expression of programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC). We then verified its predictive performance by cross-referencing its results with clinical characteristics. T...

Full description

Saved in:
Bibliographic Details
Published in:Journal of digital imaging 2021-10, Vol.34 (5), p.1073-1085
Main Authors: Jiang, Zekun, Dong, Yinjun, Yang, Linke, Lv, Yunhong, Dong, Shuai, Yuan, Shuanghu, Li, Dengwang, Liu, Liheng
Format: Article
Language:English
Subjects:
Apoptosis
B7-H1 Antigen
Carcinoma, Non-Small-Cell Lung - diagnostic imaging
Cell death
Computed tomography
Humans
Imaging
Learning algorithms
Lung cancer
Lung Neoplasms - diagnostic imaging
Machine learning
Medicine
Medicine & Public Health
Nomograms
Non-small cell lung carcinoma
Patients
PD-L1 protein
Performance prediction
Prediction models
Predictive Value of Tests
Radiology
Radiomics
Regression models
Retrospective Studies
Small cell lung carcinoma
Tomography, X-Ray Computed
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Here, we used pre-treatment CT images to develop and evaluate a radiomic signature that can predict the expression of programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC). We then verified its predictive performance by cross-referencing its results with clinical characteristics. This two-center retrospective analysis included 125 patients with histologically confirmed NSCLC. A total of 1287 hand-crafted radiomic features were observed from manually determined tumor regions. Valuable features were then selected with a ridge regression-based recursive feature elimination approach. Machine learning–based prediction models were then built from this and compared each other. The final radiomic signature was built using logistic regression in the primary cohort, and then tested in a validation cohort. Finally, we compared the efficacy of the radiomic signature to the clinical model and the radiomic-clinical nomogram. Among the 125 patients, 89 were classified as having PD-L1 positive expression. However, there was no significant difference in PD-L1 expression levels determined by clinical characteristics ( P  = 0.109–0.955). Upon selecting 9 radiomic features, we found that the logistic regression-based prediction model performed the best (AUC = 0.96, P  
ISSN:0897-1889
1618-727X
DOI:10.1007/s10278-021-00484-9