Harnessing unsupervised machine learning with [18F]FDG PET/CT to develop a composite model for predicting overall survival in cervical cancer patients undergoing concurrent chemoradiotherapy
Integrating radiomic data with inflammatory markers using unsupervised two-way clustering offered a robust approach for predicting survival outcomes in cervical cancer patients. This methodology presented a promising avenue for personalized patient management, potentially leading to more informed treatment decisions and improved outcomes.
Authors
- Shengming Deng (ORCID: https://orcid.org/0000-0002-1450-7721)
- Shushan Ge (ORCID: https://orcid.org/0000-0003-2812-1376)
- Min Zhou (ORCID: https://orcid.org/0000-0002-6831-3033)
- Bin Zhang (ORCID: https://orcid.org/0000-0002-6286-6227)
- Lian Wang (ORCID: https://orcid.org/0000-0001-7630-130X)
- Jihui Li (ORCID: https://orcid.org/0000-0002-4748-0692)
- Jinyu Shi (ORCID: https://orcid.org/0000-0002-7612-6107)
- Jiangqin Han
Institutions
- Soochow University (CN)
- Mianyang Central Hospital (CN)
- First Affiliated Hospital of Soochow University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-05-02
- DOI
- https://doi.org/10.3389/fonc.2025.1486654
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Jiangsu Commission of Health
- Soochow University