Prediction of TP53 mutations across female reproductive system pan-cancers using deep multimodal PET/CT radiogenomics
This study demonstrates that radiomic features from tumor and brown adipose tissue CT and PET images are closely associated with TP53 mutation status in gynecological tumors. This study constructed a cross-cancer TP53 model. The combined model constructed based on multi-modal imaging effectively captures TP53-related imaging phenotypes across different cancer types, and these phenotypic patterns show a significant correlation with TP53 mutation status.
Authors
- Marcin Grzegorzek (ORCID: https://orcid.org/0000-0003-4877-8287)
- Md Mamunur Rahaman (ORCID: https://orcid.org/0000-0003-2268-2092)
- Tao Jiang (ORCID: https://orcid.org/0000-0001-8937-679X)
- Li‐Qun Chen (ORCID: https://orcid.org/0000-0002-3694-0833)
- Tianming Du (ORCID: https://orcid.org/0000-0001-7509-0440)
- Xuanyi Li (ORCID: https://orcid.org/0000-0003-4367-3005)
Institutions
- UNSW Sydney (AU)
- Chengdu University of Traditional Chinese Medicine (CN)
- China Medical University (CN)
- Northeastern University (CN)
- University of Lübeck (DE)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-09-01
- DOI
- https://doi.org/10.3389/fmed.2025.1608652
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Department of Science and Technology of Sichuan Province