Differentiating high-grade patterns and predominant subtypes for IASLC grading in invasive pulmonary adenocarcinoma using radiomics and clinical-semantic features
Predicting HGP and PS using radiomics can achieve accurate IASLC grading in ADCs. Such a two-step radiomics model may provide precise preoperative diagnosis, thereby supporting treatment planning.
Authors
- Xiaonan Cui (ORCID: https://orcid.org/0000-0002-4019-6680)
- Jiping Xie (ORCID: https://orcid.org/0000-0002-8602-2774)
- Jingxiong Li (ORCID: https://orcid.org/0000-0002-6519-5043)
- Dongsheng Yue
- Sunyi Zheng (ORCID: https://orcid.org/0000-0002-9005-4875)
- Zhaoxiang Ye (ORCID: https://orcid.org/0000-0003-3157-8393)
- Keyi Bian
- Jing Wang
- Jing Liang (ORCID: https://orcid.org/0000-0002-2761-4657)
- Wenjia Zhang (ORCID: https://orcid.org/0009-0001-1428-0977)
- Jiaxin Liu (ORCID: https://orcid.org/0009-0000-5496-3011)
Institutions
- Tianjin University of Traditional Chinese Medicine (CN)
- Zhejiang University of Science and Technology (CN)
- Shanxi Medical University (CN)
- Tianjin Medical University Cancer Institute and Hospital (CN)
- Second Hospital of Shanxi Medical University (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1186/s40644-025-00864-2
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- National Natural Science Foundation of China