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

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Abstract

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 < 0.001). In the external cohort, our radiomic signature showed an AUC of 0.85, which outperformed both the clinical model (AUC = 0.38, P < 0.001) and the radiomics-nomogram model (AUC = 0.61, P < 0.001). Our CT-based hand-crafted radiomic signature model can effectively predict PD-L1 expression levels, providing a noninvasive means of better understanding PD-L1 expression in patients with NSCLC.

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Funding

This study was funded by the National Natural Science Foundation of China (NSFC61971271, NSFC81701656, NSFC81172133, NSFC81372413), the Taishan Scholars Project of Shandong Province (Tsqn20161023) and the Primary Research and Development Plan of Shandong Province (No. 2018GGX101018, No. 2019QYTPY020), the Special Fund for Scientific Research in the Public Interest (201402011), the Projects of Medical and Health Technology Development Program in Shandong Province (2014WS0058), and the Outstanding Youth Natural Science Foundation of Shandong Province (JQ201423).

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Author notes

  1. Zekun Jiang and Yinjun Dong contributed equally to this work.

Authors and Affiliations

  1. Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, 250358, Shandong, China

    Zekun Jiang & Dengwang Li

  2. Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China

    Yinjun Dong, Linke Yang, Shuai Dong & Shuanghu Yuan

  3. Liaocheng People’s Hospital, Liaocheng, 252002, Shandong, China

    Yinjun Dong

  4. Shandong University, Jinan, 250117, Shandong, China

    Yinjun Dong

  5. Department of Mathematics and Information Technology, Xingtai University, Xingtai, 054001, Hebei, China

    Yunhong Lv

  6. Department of Mathematics and Statistics, University of Windsor, Windsor, ON, N9B 3P4, Canada

    Yunhong Lv

  7. Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Liheng Liu

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  1. Zekun Jiang
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Correspondence to Shuanghu Yuan, Dengwang Li or Liheng Liu.

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Jiang, Z., Dong, Y., Yang, L. et al. CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study. J Digit Imaging 34, 1073–1085 (2021). https://doi.org/10.1007/s10278-021-00484-9

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