Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features
Through artificial intelligence (AI) algorithms, the PET/CT metabolic and radiomics features combined with clinical features could improve the accuracy of staging prediction.
Authors
- Chengguang Zhu (ORCID: https://orcid.org/0000-0002-7737-4225)
- Yongsong Wu
- Sijia Gu
- Wei Feng Shen (ORCID: https://orcid.org/0000-0002-7093-2001)
- Shanshan Cheng (ORCID: https://orcid.org/0000-0003-2676-7341)
- Yue Zhang (ORCID: https://orcid.org/0000-0003-2879-0801)
- Shuicheng Yan (ORCID: https://orcid.org/0000-0001-8906-3777)
- Yu Wang (ORCID: https://orcid.org/0009-0008-1769-9387)
- Meixuan Wu
- Chao Wang
- Weixia Zhang (ORCID: https://orcid.org/0000-0002-3634-2630)
- Jiani Yang
- Shilin Xu (ORCID: https://orcid.org/0000-0002-3300-3203)
Institutions
- Shanghai Jiao Tong University (CN)
- Renji Hospital (CN)
- Shanghai First Maternity and Infant Hospital (CN)
Publication Details
- Journal
- Journal of Cancer Research and Clinical Oncology
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1007/s00432-025-06134-9
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality
- Fundamental Research Funds for the Central Universities