Thyroid carcinoma is one of the leading malignancies in Thailand increasingly prevalent in the female population. Fine-needle aspiration (FNA) cytology is a widely used diagnostic tool for evaluation of thyroid nodules and thyroid cancer. Thyroid FNA is a routine procedure universally performed in Thai hospitals by a variety of clinical specialists. Manual guidance is the first-line choice complemented by ultrasound assistance in selected cases. Despite national guidelines recommendations, the diagnostic criteria and terminology of the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) was slowly adopted in the local settings. Currently, the Bethesda system is actively promoted by the local professional societies as a uniform reporting system. Experience with thyroid FNA has been rarely reported to date—only a handful of publications are available in local journals. Our review, in addition to presenting various aspects of thyroid FNA in Thailand, established for the first time national references for a certain statistical outputs of TBSRTC based on the original multi-institutional cohort. The risk of malignancy in 2,017 operated thyroid nodules collected from three tertiary thyroid cancer centers was 21.7%, 14.7%, 35.9%, 44.4%, 76.7%, and 92.6% for categories I to VI, respectively. The malignancy risk in several diagnostic categories (II to IV) was higher than the risk estimated by TBSRTC and recent meta-analysis studies. We endorse the use of uniform terminology of the Bethesda system in Thailand, which will help facilitate communication among diverse medical professionals involved in the management of patients with thyroid nodules, to share local experience with the international audience.
Asian Continental Ancestry Group ; Biopsy, Fine-Needle* ; Clothing ; Cohort Studies ; Complement System Proteins ; Female ; Hand ; Humans ; Specialization ; Thailand* ; Thyroid Gland* ; Thyroid Neoplasms ; Thyroid Nodule ; Ultrasonography

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.

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.

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.

The Asian Thyroid Working Group was founded in 2017 at the 12th Asia Oceania Thyroid Association (AOTA) Congress in Busan, Korea. This group activity aims to characterize Asian thyroid nodule practice and establish strict diagnostic criteria for thyroid carcinomas, a reporting system for thyroid fine needle aspiration cytology without the aid of gene panel tests, and new clinical guidelines appropriate to conservative Asian thyroid nodule practice based on scientific evidence obtained from Asian patient cohorts. Asian thyroid nodule practice is usually designed for patient-centered clinical practice, which is based on the Hippocratic Oath, “First do not harm patients,” and an oriental filial piety “Do not harm one’s own body because it is a precious gift from parents,” which is remote from defensive medical practice in the West where physicians, including pathologists, suffer from severe malpractice climate. Furthermore, Asian practice emphasizes the importance of resource management in navigating the overdiagnosis of low-risk thyroid carcinomas. This article summarizes the Asian Thyroid Working Group activities in the past 7 years, from 2017 to 2023, highlighting the diversity of thyroid nodule practice between Asia and the West and the background reasons why Asian clinicians and pathologists modified Western systems significantly.

Background: Assessing nuclear features is diagnostically challenging in the aspect of thyroid pathology. The aim of this study was to determine whether pathologists could distinguish BRAF-like and RAS-like nuclear features morphologically and identify morphological features to differentiate thyroid tumors with RAS-like mutations from encapsulated papillary thyroid carcinoma (PTC) with predominant follicular growth and BRAFV600E mutation. Methods: Representative whole slide images of 16 encapsulated thyroid tumors with predominant follicular growth were reviewed by 12 thyroid pathologists using a web browser-based image viewer. Total nuclear score was calculated from semi-quantitatively scored eight nuclear features. The molecular profile of RAS and BRAF genes was determined by Sanger sequencing. Results: Total nuclear score ranging 0 to 24 could differentiate BRAF-like tumors from RAS-like tumors with a cut-off value of score 14. The interobserver agreement was the highest for the assessment of nuclear pseudoinclusions (NPIs) but the lowest for nuclear elongation and sickle-shaped nuclei. NPIs were found in tumors with BRAFV600E mutation, but not in tumors with RAS-like mutations. Total nuclear scores were significantly higher for tumors with BRAFV600E than for those with RAS-like mutations (P<0.001). Conclusion Our results suggest that NPIs and high nuclear scores have diagnostic utility as rule-in markers for differentiating PTC with BRAFV600E mutation from benign or borderline follicular tumors with RAS-like mutations. Relaxation of rigid criteria for nuclear features resulted in an overdiagnosis of PTC. Immunostaining or molecular testing for BRAFV600E mutation is a useful adjunct for cases with high nuclear scores to identify true PTC.