Deep learning-based image analysis predicts PD-L1 status from 18F-FDG PET/CT images in non-small-cell lung cancer
This study combines the features of <sup>18</sup>F-FDG PET/CT images with clinical features using deep learning to predict the expression of PD-L1 in NSCLC, suggesting that <sup>18</sup>F-FDG PET/CT images can be conducted as biomarkers for PD-L1 expression.
Authors
- Chen Liang (ORCID: https://orcid.org/0000-0002-1552-2077)
- Chao Zuo (ORCID: https://orcid.org/0000-0002-1461-0032)
- Han Zou (ORCID: https://orcid.org/0000-0002-3574-3379)
- Jinhai Zou
- Yu Han (ORCID: https://orcid.org/0000-0002-3136-4152)
- Yingying Zhan (ORCID: https://orcid.org/0000-0003-4439-9757)
- Meiyu Zheng (ORCID: https://orcid.org/0000-0001-6810-1746)
- Yu Xing
- Chang Liu (ORCID: https://orcid.org/0000-0003-0650-1773)
Institutions
- Cangzhou Central Hospital (CN)
- Lanzhou University Second Hospital (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-09-05
- DOI
- https://doi.org/10.3389/fonc.2024.1402994
- Citations
- 8
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.35