CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study
Metastatic potential of PPGL affects surgical approaches and prognosis. CT-based radiomics deep learning signatures can predict the metastatic potential in PPGL.3. The combined model's predictive ability remains robust below the 6-cm threshold. The combined model's predictive ability remains robust below the 6-cm threshold.
Authors
- Lianggeng Gong (ORCID: https://orcid.org/0000-0002-2326-3423)
- Jinhong Zhao (ORCID: https://orcid.org/0000-0001-6590-3372)
- Linhua Zhong
- Yongming Tan
- Yongjie Zhou (ORCID: https://orcid.org/0000-0001-9578-3922)
- Yuan Zhan (ORCID: https://orcid.org/0000-0002-4108-2917)
- Fei Zou (ORCID: https://orcid.org/0000-0002-6637-3593)
- Lan Liu
- Jiayu Nan
- Qiao Zeng (ORCID: https://orcid.org/0000-0002-1027-6460)
- Xuechao Zhu
Institutions
- Nanchang University (CN)
- First Affiliated Hospital of Jiangxi Medical College (CN)
- Jiangxi Provincial Cancer Hospital (CN)
- Second Affiliated Hospital of Nanchang University (CN)
- First Affiliated Hospital of Nanchang University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-04-05
- DOI
- https://doi.org/10.1186/s13244-025-01952-4
- Citations
- 7
- Primary Topic
- Adrenal and Paraganglionic Tumors
- Type
- article
- Field-Weighted Citation Impact
- 14.04
Funders
- Nanchang University
- Wu Jieping Medical Foundation