Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis
This study analyzed 693 lymph nodes (302 positive and 391 negative) and identified 35 significant radiomic features through dimensionality reduction and selection. The three machine learning models, including the Lasso regression, Support Vector Machine (SVM), and RF radiomics models, showed.
Authors
- Liangen Xie
- Shimin Zhuang (ORCID: https://orcid.org/0000-0001-7022-1675)
- Lijiao Wu (ORCID: https://orcid.org/0009-0001-7254-5902)
- Feng Pang (ORCID: https://orcid.org/0000-0002-4073-8515)
- Tianrun Liu (ORCID: https://orcid.org/0000-0002-9049-2197)
- Mengya Du
- Danni Liu (ORCID: https://orcid.org/0000-0002-7999-716X)
- Chunlei Tan (ORCID: https://orcid.org/0009-0005-0558-0715)
- Yu Guo (ORCID: https://orcid.org/0000-0002-8303-8041)
- Jianping Qiu
Institutions
- Sun Yat-sen University (CN)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- Sun Yat-sen Memorial Hospital (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-08-12
- DOI
- https://doi.org/10.1186/s12885-025-14594-y
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Science Foundation
- National Natural Science Foundation of China
- National Natural Science Foundation of China-Guangdong Joint Fund