Radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: a multicenter study
The lightGBM-based PET radiomics model had great potential to predict the fine histological subtypes of locally advanced cervical cancer and might serve as a promising noninvasive approach for the diagnosis and management of locally advanced cervical cancer.
Authors
- Mi Lao (ORCID: https://orcid.org/0009-0009-1867-5798)
- Cheng Chang (ORCID: https://orcid.org/0000-0002-0361-2438)
- Huiling Liu (ORCID: https://orcid.org/0000-0002-3662-9842)
- Yalin Zhang (ORCID: https://orcid.org/0000-0003-4341-736X)
- Yong Yin (ORCID: https://orcid.org/0000-0003-1090-4313)
- Ruozheng Wang (ORCID: https://orcid.org/0000-0002-0524-1212)
Institutions
- Xinjiang Medical University (CN)
- Shandong Tumor Hospital (CN)
- Tumor Hospital of Xinjiang Medical University (CN)
- Binzhou People's Hospital (CN)
- People's Hospital of Xinjiang Uygur Autonomous Region (CN)
- Xinjiang Uygur Autonomous Region Uygur Medicine Hospital (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-09-17
- DOI
- https://doi.org/10.3389/fonc.2024.1346336
- Citations
- 7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.83