Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning
We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.
Authors
- Shanop Shuangshoti (ORCID: https://orcid.org/0000-0003-3416-337X)
- Somboon Keelawat (ORCID: https://orcid.org/0000-0002-4180-7914)
- Minh‐Khang Le (ORCID: https://orcid.org/0000-0002-4571-0888)
- Truong Phan-Xuan Nguyen (ORCID: https://orcid.org/0000-0001-6965-5863)
- Nakarin Kitkumthorn (ORCID: https://orcid.org/0000-0003-0616-6039)
- Sittiruk Roytrakul (ORCID: https://orcid.org/0000-0003-3696-8390)
Institutions
- National Science and Technology Development Agency (TH)
- Chulalongkorn University (TH)
- Mahidol University (TH)
- University of Yamanashi Hospital (JP)
- University of Yamanashi (JP)
- National Center for Genetic Engineering and Biotechnology (TH)
Publication Details
- Journal
- Journal of Pathology and Translational Medicine
- Published
- 2024-10-23
- DOI
- https://doi.org/10.4132/jptm.2024.09.14
- Citations
- 2
- Primary Topic
- Thyroid Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.72
Funders
- Chulalongkorn University
- Faculty of Medicine, Chulalongkorn University