Development of a model to predict Ki-67 expression status in non-Hodgkin’s lymphoma based on PET radiomics
The radiomics model derived from <sup>18</sup>F-FDG PET demonstrates superior predictive performance for Ki-67 expression status compared to T/MB. The combined model further improves prediction accuracy, highlighting its potential clinical applicability.
Authors
- Nan Xu (ORCID: https://orcid.org/0000-0001-9912-2347)
- Pengfei Tong
- Arfa Azhar (ORCID: https://orcid.org/0000-0002-9343-1218)
- Wang Rui-hua (ORCID: https://orcid.org/0000-0003-1536-6366)
- Fang Yuan (ORCID: https://orcid.org/0000-0002-4857-8261)
- Ke Tian (ORCID: https://orcid.org/0009-0002-2488-371X)
- Kalung Wu
Institutions
- Zhengzhou University (CN)
- United States Air Force (US)
- Henan Provincial People's Hospital (CN)
- Air Force General Hospital PLA (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-06-18
- DOI
- https://doi.org/10.3389/fonc.2025.1567152
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36