Predicting Ki-67 expression levels in non-small cell lung cancer using an explainable CT-based deep learning radiomics model
The interpretable combined model can predict Ki-67 expression in NSCLC patients. This approach may provide imaging evidence to assist clinicians in optimizing personalized therapeutic strategies.
Authors
- Jun Zhou (ORCID: https://orcid.org/0000-0001-7858-8260)
- Yongying Liu (ORCID: https://orcid.org/0000-0001-6369-4589)
- Shize Qin (ORCID: https://orcid.org/0000-0001-6084-5409)
- Xiaojun Zhou (ORCID: https://orcid.org/0000-0002-1791-8673)
- Man Li (ORCID: https://orcid.org/0000-0002-3839-0281)
- Qing Jia (ORCID: https://orcid.org/0000-0002-9463-3954)
- Chunmei Zhang (ORCID: https://orcid.org/0000-0002-0122-348X)
- Dan Su (ORCID: https://orcid.org/0009-0004-3362-0342)
- Xiufu Zhang
Institutions
- Chongqing University (CN)
- United Imaging Healthcare (China) (CN)
- Jiangjin Central Hospital (CN)
- First People's Hospital of Chongqing (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-12-10
- DOI
- https://doi.org/10.3389/fonc.2025.1655714
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00