Integration of 2D/3D deep learning and radiomics for predicting lymphovascular invasion in T1-stage invasive lung adenocarcinoma: a multicenter study
The combined model integrating radiomics, 2D DL, and 3D DL exhibits excellent performance in predicting the LVI status of patients with T1-stage invasive LUAD, and can provide key information for clinical treatment decision-making.
Authors
- Xiuhua Peng (ORCID: https://orcid.org/0000-0001-8442-2005)
- Hongxing Zhao
- Pengliang Xu
- Wen Xing (ORCID: https://orcid.org/0000-0002-1561-1459)
- Hao Hu (ORCID: https://orcid.org/0000-0003-1365-0267)
- Hongqiang Bian
- Shan Pi
- Hui Pan (ORCID: https://orcid.org/0000-0003-2413-0646)
- Wenhui Li (ORCID: https://orcid.org/0000-0002-8533-0572)
- Shiyu Zhang (ORCID: https://orcid.org/0009-0008-5615-4654)
- Dongping Deng
Institutions
- Sun Yat-sen University (CN)
- Huzhou Normal University (CN)
- Taizhou Second People's Hospital (CN)
- Huzhou Central Hospital (CN)
- Third People's Hospital of Huzhou (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- Wuxi People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-10-02
- DOI
- https://doi.org/10.3389/fonc.2025.1631013
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00