Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning
Issued Date
2025-01-01
Resource Type
ISSN
23837837
eISSN
23837845
Scopus ID
2-s2.0-85215675910
Journal Title
Journal of Pathology and Translational Medicine
Volume
59
Issue
1
Start Page
39
End Page
49
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Pathology and Translational Medicine Vol.59 No.1 (2025) , 39-49
Suggested Citation
Nguyen T.P.X., Le M.K., Roytrakul S., Shuangshoti S., Kitkumthorn N., Keelawat S. Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning. Journal of Pathology and Translational Medicine Vol.59 No.1 (2025) , 39-49. 49. doi:10.4132/jptm.2024.09.14 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/103091
Title
Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning
Corresponding Author(s)
Other Contributor(s)
Abstract
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.
