Machine Learning‐Based Prediction of Lymph Node Metastasis and Volume Using Preoperative Ultrasound Features in Papillary Thyroid Carcinoma
Machine learning-based predictive models integrating preoperative ultrasound features demonstrate robust performance in stratifying neck lymph node metastasis risk for PTC patients. These models optimize surgical planning by guiding lymph node dissection extent and individualizing treatment strategies, potentially reducing unnecessary extensive surgeries. The integration of advanced computational techniques with clinical imaging provides a data-driven paradigm for preoperative risk assessment in thyroid oncology.
Authors
- Tianhan Zhou (ORCID: https://orcid.org/0000-0003-0292-3346)
- Dingcun Luo (ORCID: https://orcid.org/0000-0002-7320-4202)
- Kaiyuan Huang (ORCID: https://orcid.org/0000-0002-4301-7676)
- Yu Zhang (ORCID: https://orcid.org/0000-0003-1025-906X)
- Qianyu Wang (ORCID: https://orcid.org/0000-0001-7048-8847)
- Yuan Cai
- Tao Hu (ORCID: https://orcid.org/0000-0001-6978-6994)
- Xuanwei Huang (ORCID: https://orcid.org/0009-0004-7609-5365)
- Shuoying Qian
Institutions
- Zhejiang Chinese Medical University (CN)
- Ningbo No. 2 Hospital (CN)
- Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN)
- Ningbo University Affiliated Hospital (CN)
Publication Details
- Journal
- Journal of Clinical Ultrasound
- Published
- 2025-09-04
- DOI
- https://doi.org/10.1002/jcu.70061
- Primary Topic
- Thyroid Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00