Development and validation of a predictive model combining radiomics and deep learning features for spread through air spaces in stage T1 non-small cell lung cancer: a multicenter study
The combined model is adequate for preoperative prediction of STAS in patients with stage T1 NSCLC, outperforming the other six models in predicting STAS risk.
Authors
- Xiuhua Peng (ORCID: https://orcid.org/0000-0001-8442-2005)
- Pengliang Xu
- Wen Xing (ORCID: https://orcid.org/0000-0002-1561-1459)
- Hao Hu (ORCID: https://orcid.org/0000-0003-1365-0267)
- Dan Jia (ORCID: https://orcid.org/0000-0002-6464-8215)
- Wenhui Li (ORCID: https://orcid.org/0000-0002-8533-0572)
- Shiyu Zhang (ORCID: https://orcid.org/0009-0008-5615-4654)
- Huanming Yu
- Shengxu Zhi
Institutions
- Huzhou Normal University (CN)
- Taizhou Second People's Hospital (CN)
- Huzhou Central Hospital (CN)
- Third People's Hospital of Huzhou (CN)
- Wuxi People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-05-08
- DOI
- https://doi.org/10.3389/fonc.2025.1572720
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Medical Science and Technology Project of Zhejiang Province