Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning
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 v...
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Korean Society of Pathologists & the Korean Society for Cytopathology
2025-01-01
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| Series: | Journal of Pathology and Translational Medicine |
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| Online Access: | http://www.jpatholtm.org/upload/pdf/jptm-2024-09-14.pdf |
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| author | Truong Phan-Xuan Nguyen Minh-Khang Le Sittiruk Roytrakul Shanop Shuangshoti Nakarin Kitkumthorn Somboon Keelawat |
| author_facet | Truong Phan-Xuan Nguyen Minh-Khang Le Sittiruk Roytrakul Shanop Shuangshoti Nakarin Kitkumthorn Somboon Keelawat |
| author_sort | Truong Phan-Xuan Nguyen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a5aa1d2f90864f4b8a4560fe2aa176fd |
| institution | Kabale University |
| issn | 2383-7837 2383-7845 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Korean Society of Pathologists & the Korean Society for Cytopathology |
| record_format | Article |
| series | Journal of Pathology and Translational Medicine |
| spelling | doaj-art-a5aa1d2f90864f4b8a4560fe2aa176fd2025-01-16T08:11:39ZengKorean Society of Pathologists & the Korean Society for CytopathologyJournal of Pathology and Translational Medicine2383-78372383-78452025-01-01591394910.4132/jptm.2024.09.1417130Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learningTruong Phan-Xuan Nguyen0Minh-Khang Le1Sittiruk Roytrakul2Shanop Shuangshoti3Nakarin Kitkumthorn4Somboon Keelawat5 Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand Department of Pathology, University of Yamanashi, Chuo City, Japan Functional Proteomics Technology Laboratory, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathumthani, Thailand Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand Department of Oral Biology, Faculty of Dentistry, Mahidol University, Bangkok, Thailand Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, ThailandBackground 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.http://www.jpatholtm.org/upload/pdf/jptm-2024-09-14.pdffollicular pattern thyroid tumorsthyroid carcinomamachine learning, proteomicshistological diagnosis |
| spellingShingle | Truong Phan-Xuan Nguyen Minh-Khang Le Sittiruk Roytrakul Shanop Shuangshoti Nakarin Kitkumthorn Somboon Keelawat Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning Journal of Pathology and Translational Medicine follicular pattern thyroid tumors thyroid carcinoma machine learning, proteomics histological diagnosis |
| title | Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning |
| title_full | Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning |
| title_fullStr | Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning |
| title_full_unstemmed | Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning |
| title_short | Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning |
| title_sort | diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein based machine learning |
| topic | follicular pattern thyroid tumors thyroid carcinoma machine learning, proteomics histological diagnosis |
| url | http://www.jpatholtm.org/upload/pdf/jptm-2024-09-14.pdf |
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