Correlation between PD-L1 expression and radiomic features in early-stage lung adenocarcinomas manifesting as ground-glass nodules

Wenjia Shi; Zhen Yang; Minghui Zhu; Chenxi Zou; Jie Li; Zhixin Liang; Miaoyu Wang; Hang Yu; Bo Yang; Yulin Wang; Chunsun Li; Zirui Wang; Wei Zhao; Liang'an Chen

doi:10.3389/fonc.2022.986579

Correlation between PD-L1 expression and radiomic features in early-stage lung adenocarcinomas manifesting as ground-glass nodules

Front Oncol. 2022 Sep 13:12:986579. doi: 10.3389/fonc.2022.986579. eCollection 2022.

Authors

Wenjia Shi¹, Zhen Yang², Minghui Zhu³, Chenxi Zou², Jie Li⁴, Zhixin Liang², Miaoyu Wang¹, Hang Yu¹, Bo Yang⁵, Yulin Wang⁶, Chunsun Li², Zirui Wang², Wei Zhao², Liang'an Chen²

Affiliations

¹ Department of Respiratory and Critical Medicine, Medical School of Chinese People's Liberation Army, Beijing, China.
² Department of Respiratory and Critical Medicine, Chinese People's Liberation Army General Hospital, Beijing, China.
³ Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.
⁴ Department of Pathology, Chinese People's Liberation Army General Hospital, Beijing, China.
⁵ Department of Thoracic Surgery, Chinese People's Liberation Army General Hospital, Beijing, China.
⁶ Department of Radiology, Chinese People's Liberation Army General Hospital, Beijing, China.

Abstract

Background: Immunotherapy might be a promising auxiliary or alternative systemic treatment for early-stage lung adenocarcinomas manifesting as ground-glass nodules (GGNs). This study intended to investigate the PD-L1 expression in these patients, and to explore the non-invasive prediction model of PD-L1 expression based on radiomics.

Methods: We retrospectively analyzed the PD-L1 expression of patients with postoperative pathological diagnosis of lung adenocarcinomas and with imaging manifestation of GGNs, and divided patients into positive group and negative group according to whether PD-L1 expression ≥1%. Then, CT-based radiomic features were extracted semi-automatically, and feature dimensions were reduced by univariate analysis and LASSO in the randomly selected training cohort (70%). Finally, we used logistic regression algorithm to establish the radiomic models and the clinical-radiomic combined models for PD-L1 expression prediction, and evaluated the prediction efficiency of the models with the receiver operating characteristic (ROC) curves.

Results: A total of 839 "GGN-like lung adenocarcinoma" patients were included, of which 226 (26.9%) showed positive PD-L1 expression. 779 radiomic features were extracted, and 9 of them were found to be highly corelated with PD-L1 expression. The area under the curve (AUC) values of the radiomic models were 0.653 and 0.583 in the training cohort and test cohort respectively. After adding clinically significant and statistically significant clinical features, the efficacy of the combined model was slightly improved, and the AUC values were 0.693 and 0.598 respectively.

Conclusions: GGN-like lung adenocarcinoma had a fairly high positive PD-L1 expression rate. Radiomics was a hopeful noninvasive method for predicting PD-L1 expression, with better predictive efficacy in combination with clinical features.

Keywords: PD-L1; ground-glass nodules; lung adenocarcinoma; prediction model; radiomics.