Unsupervised learning-based quantitative analysis of CT intratumoral subregions predicts risk stratification of bladder cancer patients
The subregional radiomics model based on preoperative CT shows potential value in predicting muscle invasion and AJCC stage. This noninvasive risk stratification tool may offer supportive insights into surgical approaches and treatment decisions for BCa patients.
Authors
- Rongchao Shi
- Na Li (ORCID: https://orcid.org/0009-0003-8466-3388)
- Ximing Wang (ORCID: https://orcid.org/0000-0001-5136-8057)
- Hexiang Wang (ORCID: https://orcid.org/0000-0003-1027-055X)
- Kui Sun (ORCID: https://orcid.org/0000-0002-6657-3093)
- Siyu Wu (ORCID: https://orcid.org/0009-0008-3824-922X)
- Ying Wang (ORCID: https://orcid.org/0009-0008-3662-2692)
Institutions
- Qingdao University (CN)
- Capital Medical University (CN)
- Zhangqiu City People's Hospital (CN)
- Shandong Provincial Hospital (CN)
- Affiliated Hospital of Qingdao University (CN)
- Beijing Friendship Hospital (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- BMC Medicine
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1186/s12916-025-04163-2
- 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