Application and validation of the machine learning-based multimodal radiomics model for preoperative prediction of lateral lymph node metastasis in papillary thyroid carcinoma
The GBM-based multimodal prediction model accurately predicts LLNM in PTC patients preoperatively. This non-invasive, interpretable tool enables individualized risk assessment, potentially reducing missed metastases requiring secondary surgery, thereby supporting precise treatment decisions in PTC management.
Authors
- Shuiqing Liu (ORCID: https://orcid.org/0000-0003-3029-2051)
- Yong Jiang (ORCID: https://orcid.org/0000-0002-8087-5717)
- Yuxin Yang (ORCID: https://orcid.org/0000-0002-1742-2328)
- Renyi Qin (ORCID: https://orcid.org/0000-0003-1654-1285)
- Jia‐Wei Feng (ORCID: https://orcid.org/0000-0002-1984-0646)
- Ancheng Qin
Institutions
- Soochow University (CN)
- The First People's Hospital of Changzhou (CN)
- Suzhou Municipal Hospital (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
- First Affiliated Hospital of Soochow University (CN)
- Changzhou Third People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-08-19
- DOI
- https://doi.org/10.3389/fendo.2025.1618902
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Changzhou Science and Technology Bureau