A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study
The CT radiomics model could identify HER2 status in bladder cancer. The random forest model showed a more robust and accurate performance. The model demonstrated favorable interpretability through SHAP method.
Authors
- Xuesong Bai (ORCID: https://orcid.org/0000-0003-4324-1181)
- Fajin Lv (ORCID: https://orcid.org/0000-0002-1100-5494)
- Shao‐Hao Chen (ORCID: https://orcid.org/0000-0002-7243-4428)
- Mingzhao Xiao (ORCID: https://orcid.org/0000-0001-6989-5971)
- Yingjie Xv (ORCID: https://orcid.org/0000-0001-5585-6058)
- Zongjie Wei
- Yang Li (ORCID: https://orcid.org/0000-0003-4399-1505)
- Siwen Yin
- Yongpeng Xie (ORCID: https://orcid.org/0009-0007-7790-3194)
Institutions
- Fujian Medical University (CN)
- Chongqing University (CN)
- First Affiliated Hospital of Fujian Medical University (CN)
- Fuling Center Hospital of Chongqing (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1186/s13244-024-01840-3
- Citations
- 20
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 11.46
Funders
- Natural Science Foundation of Chongqing