EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer
An efficient and accurate EUS-based radiomic model incorporating intratumoral and peritumoral radiomic features was proposed and validated to accurately distinguish PNETs from pancreatic cancer. This research has the potential to offer novel perspectives on enhancing the clinical utility of EUS in the prediction of PNETs.
Authors
- Haixiao Wei (ORCID: https://orcid.org/0000-0002-9038-7151)
- Haixing Jiang (ORCID: https://orcid.org/0000-0003-2974-2035)
- Shuangyang Mo
- Shanyu Qin
- Huaying Zhao (ORCID: https://orcid.org/0009-0008-0330-5906)
- Yi Nan (ORCID: https://orcid.org/0000-0001-7323-0729)
- Yingwei Wang (ORCID: https://orcid.org/0000-0002-6764-4367)
- Haiyan Qin (ORCID: https://orcid.org/0000-0002-2978-1061)
- Fengyan Qin
Institutions
- Guangxi Medical University (CN)
- First Affiliated Hospital of GuangXi Medical University (CN)
- Fourth Affiliated Hospital of Guangxi Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-03-04
- DOI
- https://doi.org/10.3389/fonc.2025.1442209
- Citations
- 2
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 2.09