Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT
This study offers a promising approach for identifying t-FL non-invasively by radiomics analysis on PET/CT images. The developed t-FL scoring system provides a valuable tool for clinical decision-making, potentially improving patient management and outcomes.
Authors
- Rong Tian (ORCID: https://orcid.org/0000-0002-5191-9004)
- Chong Jiang (ORCID: https://orcid.org/0000-0002-8107-4719)
- Zekun Jiang (ORCID: https://orcid.org/0000-0002-3178-7761)
- Chunjun Qian (ORCID: https://orcid.org/0000-0003-2466-4517)
- Xin Li (ORCID: https://orcid.org/0000-0001-6163-1103)
- Yue Teng (ORCID: https://orcid.org/0000-0002-8284-5044)
- Hang Zhou (ORCID: https://orcid.org/0000-0003-2719-790X)
- Chongyang Ding
- Qiuhui Jiang (ORCID: https://orcid.org/0000-0002-2613-706X)
Institutions
- Sichuan University (CN)
- First Affiliated Hospital of Xiamen University (CN)
- West China Hospital of Sichuan University (CN)
- Changzhou Institute of Technology (CN)
- Nanjing Drum Tower Hospital (CN)
- Changzhou No.2 People's Hospital (CN)
- Jiangsu Province Hospital (CN)
- Qilu Hospital of Shandong University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- BMC Medicine
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1186/s12916-025-03893-7
- Citations
- 7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 9.52
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Sichuan University
- West China Hospital, Sichuan University