Development and validation of models based on clinical and CT features: multivariate analysis for predicting vascular invasion in non-small cell lung cancer
We developed and validated a model for predicting LVI in NSCLC based on clinical and CT image features. The model developed in this study has potential application value in predicting LVI in NSCLC. It provides a new, operable, and non-invasive technique for clinical identification of high-risk patients and may help clinical selection of appropriate treatment.
Authors
- Ting Yang (ORCID: https://orcid.org/0000-0002-5113-8611)
- Zongyu Xie (ORCID: https://orcid.org/0000-0002-7059-5260)
- Jian Wang (ORCID: https://orcid.org/0000-0003-1194-9046)
- Cheng Yan (ORCID: https://orcid.org/0000-0001-8905-5718)
- Fengcai Wei (ORCID: https://orcid.org/0000-0002-3509-9640)
- Hengfeng Shi
- Fengjuan Tian
- Xiaoyu Han (ORCID: https://orcid.org/0000-0001-6121-596X)
- Jieling Zhu
Institutions
- Sir Run Run Shaw Hospital (CN)
- Taizhou Municipal Hospital (CN)
- Anqing City Hospital (CN)
- First Affiliated Hospital of Bengbu Medical College (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-08-21
- DOI
- https://doi.org/10.21037/qims-24-1886
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00