Integrating peritumor and tumor CT radiomics features in predicting local control after SBRT in patients with pulmonary oligometastases
The Model-GPC, integrating clinical and radiomic features, accurately predicts post-SBRT local control in pulmonary oligometastases. Incorporating peritumoral features and SHAP analysis enhances prediction accuracy, offering insights to optimize SBRT strategies.
Authors
- Weixiang Zhong (ORCID: https://orcid.org/0000-0002-5493-893X)
- Senxiang Yan (ORCID: https://orcid.org/0000-0001-6902-0892)
- Yifu Ding (ORCID: https://orcid.org/0000-0002-4629-5858)
- Feng Zhao (ORCID: https://orcid.org/0000-0002-4441-9992)
- Yao Lu (ORCID: https://orcid.org/0000-0002-5345-5231)
- Yiqi Wang (ORCID: https://orcid.org/0009-0002-4989-3674)
- Jing Yang (ORCID: https://orcid.org/0000-0001-6646-3401)
- Wenguang He (ORCID: https://orcid.org/0009-0007-6540-2050)
- Ge Ren
- Danni Chen (ORCID: https://orcid.org/0000-0002-6003-8284)
Institutions
- Hong Kong Polytechnic University (HK)
- Zhejiang Cancer Hospital (CN)
- University of Hong Kong - Shenzhen Hospital (CN)
- Zhejiang University-University of Edinburgh Institute (CN)
- Hangzhou Medical College (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Radiation Oncology
- Published
- 2025-08-14
- DOI
- https://doi.org/10.1186/s13014-025-02712-w
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- National Natural Science Foundation of China