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Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning
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Original Article Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning
Truong Phan-Xuan Nguyen1orcid , Minh-Khang Le2orcid , Sittiruk Roytrakul3orcid , Shanop Shuangshoti1,4orcid , Nakarin Kitkumthorn5orcid , Somboon Keelawat1,6orcid

DOI: https://doi.org/10.4132/jptm.2024.09.14 [Epub ahead of print]
Published online: October 24, 2024
1Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
2Department of Pathology, University of Yamanashi, Chuo City, Japan
3Functional Proteomics Technology Laboratory, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathumthani, Thailand
4Chulalongkorn GenePRO Center, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
5Department of Oral Biology, Faculty of Dentistry, Mahidol University, Bangkok, Thailand
6Precision Pathology of Neoplasia Research Group, Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
Corresponding author:  Nakarin Kitkumthorn, Tel: +66-868815947, Fax: +66-22564208, 
Email: nakarinkit@gmail.com
Received: 23 July 2024   • Revised: 11 September 2024   • Accepted: 14 September 2024
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Background
Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples.
Methods
We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples.
Results
We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC > 0.5.
Conclusions
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.

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