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Utility of CT Radiomics for Prediction of PD-L1 Expression in Advanced Lung Adenocarcinomas

Authors
 Jiyoung Yoon  ;  Young Joo Suh  ;  Kyunghwa Han  ;  Hyoun Cho  ;  Hye-Jeong Lee  ;  Jin Hur  ;  Byoung Wook Choi 
Citation
 THORACIC CANCER, Vol.11(4) : 993-1004, 2020-04 
Journal Title
 THORACIC CANCER 
ISSN
 1759-7706 
Issue Date
2020-04
Keywords
Computed tomography ; immunotherapy ; lung adenocarcinoma ; programmed death ligand 1 ; radiomics
Abstract
Background: We aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD-L1) expression in advanced stage lung adenocarcinoma. Methods: This retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD-L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c-statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability. Results: Among 153 patients, 53 patients were classified as PD-L1 positive and 100 patients as PD-L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad-score by radiomic analysis was higher in the PD-L1 positive group than in the PD-L1 negative group with a statistical significance (-0.378 ± 1.537 vs. -1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c-statistic = 0.646 vs. 0.550, P = 0.0299). Conclusions: Quantitative CT radiomic features can predict PD-L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression. Key points: Significant findings of the study Quantitative CT radiomic features can help predict PD-L1 expression, whereas none of the qualitative imaging findings is associated with PD-L1 positivity. What this study adds A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression.
Files in This Item:
T202001301.pdf Download
DOI
10.1111/1759-7714.13352
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Suh, Young Joo(서영주) ORCID logo https://orcid.org/0000-0002-2078-5832
Yoon, Jiyoung(윤지영)
Lee, Hye Jeong(이혜정) ORCID logo https://orcid.org/0000-0003-4349-9174
Choi, Byoung Wook(최병욱) ORCID logo https://orcid.org/0000-0002-8873-5444
Hur, Jin(허진) ORCID logo https://orcid.org/0000-0002-8651-6571
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/176085
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