Elevated Cancer-Associated Hyaluronan Correlates With Diagnosis and Lymph Node Metastasis of Papillary Thyroid Cancer.

The glycosaminoglycan hyaluronan (HA) plays an important role in tumor progression. However, its biological and clinical significance in papillary thyroid cancer (PTC) remains unknown. Immunohistochemistry was performed to examine HA expression in tissues from PTC patients. Two PTC cell lines were treated with HA synthesized inhibitor against HA production to assess its function. Serum HA levels from 107 PTC patients, 30 Hashimoto thyroiditis patients, and 45 normal controls (NC) were measured by chemiluminescence immunoassay. HA levels in fine needle aspiration (FNA) washouts obtained from thyroid nodules and lymph nodes (LNs) were measured by chemiluminescence immunoassay. Area under the curve (AUC) was computed to evaluate HA's clinical value. HA was highly expressed in PTC. Reducing HA production significantly inhibited PTC cell proliferation and invasion. Importantly, serum HA levels in PTC were significantly higher than those in NCs and Hashimoto thyroiditis and allowed distinguishing of thyroid cancers from NCs with high accuracy (AUC = 0.782). Moreover, elevated serum HA levels in PTC correlate with LN metastasis. HA levels in FNA washouts from PTC patients were significantly higher than those in benign controls, with a high AUC value (0.8644) for distinguishing PTC from benign controls. Furthermore, HA levels in FNA washouts from metastatic LN were significantly higher than those in nonmetastatic LN, with a high AUC value (0.8007) for distinguishing metastatic LNs from nonmetastatic LNs. HA levels in serum and FNA washout exhibited a potential significance for PTC diagnosis and an indicator for LN metastasis in patients with PTC.

Identifying potential breath biomarkers for early diagnosis of papillary thyroid cancer based on solid-phase microextraction gas chromatography-high resolution mass spectrometry with metabolomics.

INTRODUCTION: Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population. OBJECTIVES: This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs). METHODS: We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers. RESULTS: The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.

Imprinted gene detection effectively improves the diagnostic accuracy for papillary thyroid carcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the most frequent histological type of thyroid carcinoma. Although an increasing number of diagnostic methods have recently been developed, the diagnosis of a few nodules is still unsatisfactory. Therefore, the present study aimed to develop and validate a comprehensive prediction model to optimize the diagnosis of PTC. METHODS: A total of 152 thyroid nodules that were evaluated by postoperative pathological examination were included in the development and validation cohorts recruited from two centres between August 2019 and February 2022. Patient data, including general information, cytopathology, imprinted gene detection, and ultrasound features, were obtained to establish a prediction model for PTC. Multivariate logistic regression analysis with a bidirectional elimination approach was performed to identify the predictors and develop the model. RESULTS: A comprehensive prediction model with predictors, such as component, microcalcification, imprinted gene detection, and cytopathology, was developed. The area under the curve (AUC), sensitivity, specificity, and accuracy of the developed model were 0.98, 97.0%, 89.5%, and 94.4%, respectively. The prediction model also showed satisfactory performance in both internal and external validations. Moreover, the novel method (imprinted gene detection) was demonstrated to play a role in improving the diagnosis of PTC. CONCLUSION: The present study developed and validated a comprehensive prediction model for PTC, and a visualized nomogram based on the prediction model was provided for clinical application. The prediction model with imprinted gene detection effectively improves the diagnosis of PTCs that are undetermined by the current means.

Pathology diagnosis of intraoperative frozen thyroid lesions assisted by deep learning.

BACKGROUND: Thyroid cancer is a common thyroid malignancy. The majority of thyroid lesion needs intraoperative frozen pathology diagnosis, which provides important information for precision operation. As digital whole slide images (WSIs) develop, deep learning methods for histopathological classification of the thyroid gland (paraffin sections) have achieved outstanding results. Our current study is to clarify whether deep learning assists pathology diagnosis for intraoperative frozen thyroid lesions or not. METHODS: We propose an artificial intelligence-assisted diagnostic system for frozen thyroid lesions that applies prior knowledge in tandem with a dichotomous judgment of whether the lesion is cancerous or not and a quadratic judgment of the type of cancerous lesion to categorize the frozen thyroid lesions into five categories: papillary thyroid carcinoma, medullary thyroid carcinoma, anaplastic thyroid carcinoma, follicular thyroid tumor, and non-cancerous lesion. We obtained 4409 frozen digital pathology sections (WSI) of thyroid from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) to train and test the model, and the performance was validated by a six-fold cross validation, 101 papillary microcarcinoma sections of thyroid were used to validate the system's sensitivity, and 1388 WSIs of thyroid were used for the evaluation of the external dataset. The deep learning models were compared in terms of several metrics such as accuracy, F1 score, recall, precision and AUC (Area Under Curve). RESULTS: We developed the first deep learning-based frozen thyroid diagnostic classifier for histopathological WSI classification of papillary carcinoma, medullary carcinoma, follicular tumor, anaplastic carcinoma, and non-carcinoma lesion. On test slides, the system had an accuracy of 0.9459, a precision of 0.9475, and an AUC of 0.9955. In the papillary carcinoma test slides, the system was able to accurately predict even lesions as small as 2 mm in diameter. Tested with the acceleration component, the cut processing can be performed in 346.12 s and the visual inference prediction results can be obtained in 98.61 s, thus meeting the time requirements for intraoperative diagnosis. Our study employs a deep learning approach for high-precision classification of intraoperative frozen thyroid lesion distribution in the clinical setting, which has potential clinical implications for assisting pathologists and precision surgery of thyroid lesions.

Prediction Model of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma.

BACKGROUND: The objective of this study is to develop a predictive model for the assessment of cervical lymph node metastasis risk in papillary thyroid carcinoma (PTC). METHODS: A retrospective study was conducted on 212 patients with PTC who underwent initial surgical treatment from August 2022 to April 2023 in 2 hospitals. Data were randomly split into 7:3 training-validation sets. Logistic regression was used for feature selection and predictive model creation. Model performance was assessed using receiver operating characteristic (ROC) and calibration curves. Clinical utility was determined using decision curves. RESULTS: Among the 212 patients with PTC, 104 cases (49.1%) exhibited cervical lymph node metastasis, while 108 cases (50.9%) did not. Multivariate logistic regression analysis revealed that age (OR = 0.95), FT3 (OR = 0.41), tumor maximum diameter ≥0.9 cm (OR = 1.85), intratumoral microcalcifications (OR = 1.84), and suspicious lymph node on ultrasound (OR = 2.96) were independent risk factors for lymph node metastasis in PTC patients (P < 0.05). The constructed model for predicting the risk of cervical lymph node metastasis demonstrated a training set ROC curve area under the curve (AUC) of 0.742 (95% CI: 0.664 - 0.821), with a cut-off value of 0.615, specificity of 87.8%, and sensitivity of 51.4%. The validation set exhibited an AUC of 0.648 (95% CI: 0.501 - 0.788), with a cut-off value of 0.644, specificity of 91.2%, and sensitivity of 43.3%. Including the BRAF V600 E mutation did not improve the model's diagnostic performance significantly. Decision curve analysis indicated clinical feasibility of the model. CONCLUSION: The predictive model developed in this study effectively predicts lymph node metastasis risk in PTC patients by incorporating ultrasound features, demographic characteristics, and serum parameters. However, including the BRAF V600 E mutation does not significantly improve the model's diagnostic performance.

BackgroundThe objective of this study is to develop a predictive model for the assessment of cervical lymph node metastasis risk in papillary thyroid carcinoma.MethodsA retrospective study was conducted on 212 patients with papillary thyroid carcinoma who underwent initial surgical treatment at the First Affiliated Hospital of Fujian Medical University from August 2022 to April 2023. Data randomly split into 7:3 training-validation sets. Logistic regression used for feature selection and predictive model creation. Model performance assessed using ROC and calibration curves. Clinical utility determined using decision curves.ResultsAmong the 212 patients with PTC, 104 cases (49.1%) exhibited cervical lymph node metastasis, while 108 cases (50.9%) did not. Multivariate logistic regression analysis revealed that age (OR = 0.95), FT3 (OR = 0.41), tumor maximum diameter ≠¥ 0.9cm(OR = 1.85), intratumoral microcalcifications (OR = 1.84), and suspicious lymph node on ultrasound (OR = 2.96) were independent risk factors for LNM in PTC patients (P < 0.05). The constructed model for predicting the risk of cervical lymph node metastasis demonstrated a training set ROC curve area under the curve (AUC) of 0.742 (95% CI: 0.664 - 0.821), with a cut-off value of 0.615, specificity of 87.8%, and sensitivity of 51.4%. The validation set exhibited an AUC of 0.648 (95% CI: 0.501 - 0.788), with a cut-off value of 0.644, specificity of 91.2%, and sensitivity of 43.3%. Including BRAF V600E gene did not improve model's diagnostic performance significantly. Decision curve analysis indicated clinical feasibility of the model.ConclusionThe predictive model developed in this study effectively predicts lymph node metastasis risk in PTC patients by incorporating ultrasound features, demographic characteristics, and serum parameters.However, Including the BRAF V600E mutation does not significantly improve diagnosis performance.

Multi-UniFocality (MUF), in contrast to multifocality, in thyroid lesions: Relation to lymphocytic thyroiditis.

Whereas multifocality typically concerns papillary thyroid carcinoma (PTC) without specification of intrathyroidal metastatic or independent nature of tumor foci, the designation of the latter as Multi-UniFocal (MUF) may be relevant for select cases. A case series involving multifocal thyroid lesions with divergent histopathological morphology and/or molecular profile, with molecular evaluation of multiple individual tumor foci per patient based on a next-generation sequencing approach, was retrospectively reviewed. Twenty-five patient cases with multifocal thyroid lesions suggestive of MUF, with 2-6 (median 3) tumor foci per patient, were described. Tumor lesions comprised diverse histopathology, including PTC, (E)FVPTC, NIFTP, FA, FTC, and oncocytic. Morphologically similar and/or diverse tumor foci harbored different molecular alterations (suggestive of non-shared clonality); with(out) coexistent similar foci harboring identical molecular alterations; or (partly) shared molecular alterations. MUF was associated with chronic lymphocytic thyroiditis in almost half of the cases. The recognition of MUF may justify the independent clinical consideration per individual tumor focus; as separate lesions albeit within a multifocal context. The potential clinical relevance and prognostic value of MUF remain to be further established.

Prevalence and diagnostic significance of non-invasive follicular thyroid neoplasm with papillary-like nuclear features in Japan-A multi-institutional study.

This multi-institutional study investigated non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) frequency and its diagnostic significance in Japan. We reviewed 4008 thyroid nodules resected in six institutions before NIFTP was proposed. Overall, 26 cases diagnosed as non-invasive encapsulated follicular variant of papillary thyroid carcinoma (PTC) and 145 cases of follicular thyroid adenoma (FTA) were included. Of these nodules, 80.8% and 31.0%, respectively, were NIFTPs. In five institutions, NIFTPs were more commonly found in FTA than in PTC nodules. When NIFTP was included with PTC, the overall prevalence was 2.3%, with rates in five institutions below 5.0% (0.8%-4.4%). One NIFTP case with nuclear score 3 revealed nodal metastasis 2.5 years post-resection, and the carcinoma cells were immunohistochemically positive for BRAF. FTAs or NIFTPs with nuclear score 2 did not metastasize. NIFTP was more common among FTA than among PTC nodules, possibly due to underdiagnosis of PTC on nuclear findings. Considering the clinical findings, molecular pathogenesis, and therapeutic strategy in Japan, NIFTP with nuclear score 2 is not different from FTA, and use of this entity terminology is not meaningful. In contrast, NIFTP with nuclear score 3 has potential for metastasis and BRAFV600E mutation. Therefore, in NIFTP cases, nuclear scores 2 and 3 should be separately reported.

FHL1: A novel diagnostic marker for papillary thyroid carcinoma.

Although there are clear morphologic criteria for the diagnosis of papillary thyroid carcinoma (PTC), when the morphology is untypical or overlaps, accurate diagnostic indicators are necessary. Since few studies investigated the role of down-regulated genes in PTC, this article aims to further explore the molecular markers associated with PTC. We conducted bioinformatics analysis of gene microarrays of PTC and normal adjacent tissues. Besides, quantitative real-time quantitative polymerase chain reaction array and immunohistochemical staining were used to investigate the expression of the major down-regulated genes. The results indicated that several important down-regulated genes, including TLE1, BCL2, FHL1, GHR, KIT, and PPARGC1A were involved in the process of PTC. Compared to normal adjacent tissues, the mRNA expression of the major genes was down-regulated in PTC (p＜0.05). Immunohistochemically, FHL1 shows negative or low expression in PTC tissues (p＜0.05). BCL2 did not show a significant difference between PTC and normal thyroid tissues (p > 0.05). TLE1, KIT, PPARGC1A and GHR showed negative expression in both tumor and normal tissues. These results suggested that FHL1 could serve as a novel tumor marker for precise diagnosis of PTC.

Single-cell and bulk RNA sequencing reveal heterogeneity and diagnostic markers in papillary thyroid carcinoma lymph-node metastasis.

PURPOSE: Papillary thyroid carcinoma (PTC) is characterized by lymph-node metastasis (LNM), which affects recurrence and prognosis. This study analyzed PTC LNM by single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) to find diagnostic markers and therapeutic targets. METHODS: ScRNA-seq data were clustered and malignant cells were identified. Differentially expressed genes (DEGs) were identified in malignant cells of scRNA-seq and bulk RNA-seq, respectively. PTC LNM diagnostic model was constructed based on intersecting DEGs using glmnet package. Next, PTC samples from 66 patients were used to validate the two most significant genes in the diagnostic model, S100A2 and type 2 deiodinase (DIO2) by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC). Further, the inhibitory effect of DIO2 on PTC cells was verified by cell biology behavior, western blot, cell cycle analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, and xenograft tumors. RESULTS: Heterogeneity of PTC LNM was demonstrated by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. A total of 19 differential genes were used to construct the diagnostic model. S100A2 and DIO2 differ significantly at the RNA (p < 0.01) and protein level in LNM patient tissues (p < 0.001). And differed in PTC tissues with different pathologic typing (p < 0.001). Further, EdU (p < 0.001) and cell biology behavior revealed that PTC cells overexpressed DIO2 had reduced proliferative capacity. Cell cycle proteins were reduced and cells are more likely to be stuck in G2/M phase (p < 0.001). CONCLUSIONS: This study explored the heterogeneity of PTC LNM using scRNA-seq. By combining with bulk RNA-seq data, diagnostic markers were explored and the model was established. Clinical diagnostic efficacy of S100A2 and DIO2 was validated and the treatment potential of DIO2 was discovered.

Diagnostic Efficiency of ACR-TIRADS Score for Differentiating Benign and Malignant Thyroid Nodules of Various Pathological Types.

BACKGROUND Thyroid nodule prevalence reaches 65% in the general population. Hence, appropriate ultrasonic examination is key in disease monitoring and management. We investigated the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) score for diagnosis of benign and malignant thyroid nodules and pathological types. MATERIAL AND METHODS A retrospective study was conducted. According to ultrasound images, ultrasonic characteristics of benign and malignant thyroid nodules and different pathological types were analyzed using ACR-TIRADS score, and diagnostic value was determined. AUCs were compared for tumor diagnosis and differentiation. RESULTS Overall, 1675 thyroid nodules from 1614 patients were included. AUC value of papillary thyroid carcinoma (PTC) diagnosed with ACR-TIRADS was highest (0.955 [95% CI=0.946-0.965]), while that of follicular thyroid carcinoma (FTC) was lowest (0.877 [95% CI=0.843-0.912]). FTC had the highest sensitivity (95.1%) and lowest specificity (64.8%). When the cut-off value was 5.5 points, accuracy of diagnosing PTC and anaplastic thyroid carcinoma (ATC) was highest, 80.5% and 78.7% respectively. Comparison of the multi-index prediction model constructed by multivariable logistic regression analysis and prediction model constructed by ACR-TIRADS score showed, when evaluating PTC and ATC, the multi-index model was better: AUCs of PTC were 0.966 vs 0.955, and AUCs of ATC were 0.982 vs 0.952, respectively, (P<0.05). CONCLUSIONS ACR-TIRADS score-based ultrasound examination of thyroid nodules aids diagnosis of benign and malignant thyroid nodules. TIRADS criteria favor diagnosis of PTC (and ATC) over FTC. ACR-TIRADS score can help clinicians diagnose thyroid nodules quickly and earlier, exhibits good clinical value, and can prevent missed diagnoses.

The efficacy and assessment value of the level of thyroglobulin wash-out after fine-needle aspiration cytodiagnosis in the evaluation of lymph node metastasis in papillary thyroid carcinoma.

OBJECTIVE: The purpose of this study was to evaluate the efficacy and clinical value of US, FNAC,FNA-Tg and FNAC + FNA-Tg, as well as the cutoff values of FNA-Tg to evaluate LN metastasis. METHODS: We analyzed the diagnostic value of different US signs, the efficiency of US, FNAC, FNA-Tg and FNAC + FNA-Tg among the LN- and LN + groups, and the cutoff value of FNA-Tg to evaluate LN metastasis. We punctured LNs multiple times and measured the levels of FNA-Tg. Furthermore, the LNs were marked with immunohistochemical Tg and LCA to distinguish the presence of Tg in the para-cancerous tissue of the LNs. RESULTS: The s-Tg and FNA-Tg of the LN + group were higher than those of the LN- group (P = 0.018, ≤ 0.001). The LN + group had more abnormal US signs than the LN- group. The cutoff value of FNA-Tg was 3.2 ng/mL. US had a high sensitivity (92.42), but the specificity was not satisfactory (55.1). FNA-Tg had a higher sensitivity (92.42 vs. 89.39), specificity (100 vs. 93.88), and accuracy (92.42 vs. 83.27) than FNAC. However, the sensitivity of FNAC + FNA-Tg increased further, while the specificity and accuracy decreased slightly. The presence of Tg in the normal lymphocytes adjacent to the cancer was confirmed. CONCLUSION: Ultrasonography provides a noninvasive, dynamic, multidimensional assessment of LNs. With a cutoff value of 3.2 ng/mL, FNA-Tg has higher accuracy and a lower false-negative rate than various single diagnoses. However, FNAC combined with FNA-Tg does not cause additional pain to patients and offers a higher diagnostic efficacy and clinical value.

Hobnail subtype of papillary carcinoma of the thyroid-a rare case with uncommon features.

The cytological features of the hobnail variant of papillary thyroid carcinoma may be subtle. It is important to recognize this variant because it may influence the corresponding surgical treatment and follow-up due to its aggressive nature. The hobnail subtype of papillary thyroid carcinoma is a rare entity with aggressive features. It presents extrathyroidal extension or lymph nodal metastasis in a high percentage of the cases.

Digital analyses of nuclear features can help discriminate among non-invasive follicular thyroid neoplasm with papillary-like nuclear features, papillary thyroid carcinoma follicular subtype, and follicular carcinoma in cytological specimens.

BACKGROUND: As it stands, the diagnosis of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) is primarily based on histological analysis. We hypothesised that computerised analysis of nuclear images of cytological specimens could be used to differentiate NIFTP from papillary thyroid carcinoma follicular subtype (PTCFS) and follicular carcinoma (FC), influencing patient management. METHODS: We employed a retrospective analytical observational study based on nuclear morphometric variables of cytological material from thyroid nodules classified as PTCFS, NIFTP, or FC. Five cases of each entity were analysed. Cytological slides were photographed, and 1170 cells for each entity were analysed digitally. The captured images were evaluated (blindly) using the ImageJ software package. The morphometric evaluation included area, perimeter, width, height, and circularity. Numerical variables were expressed as mean, median, minimum, and maximum (min; max) values. Kruskal-Wallis and Dunn's tests were used with a 5% significance level. RESULTS: Regarding nuclear analysis, all variables differed among the three groups (p < 0.001). Given the interdependence among the variables, these data indicated that nuclear size was greatest in the NIFTP group, followed by FC and PTCFS. DISCUSSION/CONCLUSION: Our analysis of the digital images, with a focus on nuclear parameters, found significantly difference among cytological specimens from cases of NIFTP, PTCFS and FC. Thus, this tool has the potential to provide additional information that may help in the diagnosis of NIFTP, even during the preoperative period. Additional studies are needed to create protocols, evaluate the applicability of nuclear morphological and morphometric parameters-focusing on digital pathology-and create algorithms and tools to assist cytopathologists with their diagnostic routines.

Thyroid poorly differentiated carcinoma metastatic to pancreas diagnosed by fine-needle aspiration and demonstrating a novel BRAF fusion.

Differentiating pancreatic duct adenocarcinoma from metastasis can be challenging by morphology alone. Metastasis from a classic papillary thyroid carcinoma can present as a poorly differentiated carcinoma and mimic pancreatic ductal adenocarcinoma's morphology.

Accuracy of ultrasound-guided fine-needle aspiration cytology in evaluation of thyroid nodules using different ultrasonographic and cytological features.

BACKGROUND: Fine-needle aspiration cytology (FNAC) is a reliable method for preoperative evaluation of thyroid nodules particularly if ultrasound-guided (USG-FNAC). The main purpose of this study is to evaluate the efficacy of USG-FNAC and its accuracy. METHODS: We retrospectively studied 212 thyroidectomy cases with preoperative ultrasonography and FNAC data during the period 2015-2022 using TI-RADS for final ultrasound diagnosis and Bethesda system for cytological diagnosis. RESULTS: The studied cases were 200 females and 12 males. Thyroid cancer was more prevalent under 20 years old (78.5%). Papillary thyroid carcinoma comprises 84% of all cancer cases. Significant ultrasound features (p-value <0.05) favour malignancy were hypoechogenicity (66%), mixed echogenicity (84%), irregular border (61%), microcalcification (68%) and rim halo (63.6%). Malignancy was found in 21% of TI-RADS-2, 65% of TI-RADS-4 and 100% of TI-RADS-5. There is a significant difference between different categories of Bethesda system. All cases in Cat-VI were malignant (100%). Malignancy was also found in 81% of Cat-V, 20% of Cat-IV, 33% of Cat-III, 16% of Cat-II and 43% of Cat-I. Cytological features consistent with malignancy were as follows: grooving (94%), nuclear irregularities (89%), nuclear pseudoinclusion (89%) and little colloid (82%). In our study, USG-FNAC sensitivity was 83%, specificity 85%, PPV 85%, NPV 83% and accuracy 84%. CONCLUSION: Ultrasound features in favour of malignancy in thyroid nodules are hypoechoic or complex echogenicity, irregular border, punctuate calcification and presence of rim halo. Cytological features in favour of malignancy are grooving, nuclear irregularities, nuclear pseudoinclusion and little or absent colloid.

EphB3 protein is a potential ancillary diagnostic biomarker for thyroid cancers.

OBJECTIVE: To investigate the expression of ephrin type B receptor 3 (EphB3) in thyroid tumors and its usage as an ancillary diagnostic biomarker for thyroid tumors. METHODS: Formalin-fixed and paraffin-embedded (FFPE) tissue samples (78 cases) and FNAC samples (57 cases) were assessed with the EphB3 antibody using immunohistochemistry. PTC and other thyroid follicular tumors were compared regarding their EphB3 expression. Sanger sequencing was used to assess for the presence of a BRAF V600E mutation. RESULTS: EphB3 was positive in 81.8 % (27/33) of papillary thyroid carcinoma (PTC), 83.3 % (5/6) of medullary thyroid carcinoma (MTC), 25 % (1/4) of hyperplastic/adenomatoid nodule (HN), 14.3 % (1/7) of follicular adenoma (FA), and negative in follicular tumors of uncertain malignant potential (FT-UMP) (0/13), noninvasive follicular neoplasm with papillary-like nuclear features (NIFTP) (0/7), thyroid follicular carcinoma (TFC) (0/4), Hashimoto's thyroiditis (0/4), and normal thyroid follicular tissues (0/33). In cellular blocks, EphB3 was positive in 87.1 % (20/23) of PTC, 75 % (3/4) of MTC, 20 % (2/10) of HN, and negative in atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) (0/20) and normal thyroid follicular cells (0/10). CONCLUSION: EphB3 is expressed in the majority of PTC, but less so in benign follicular nodules. EphB3 expression in fine needle aspiration cytology (FNAC) specimens can be used as a diagnostic tool to differentiate thyroid cancer from other follicular lesions in its differential diagnosis, especially AUS/FLUS and PTC.

Evaluation of the follicular patterned thyroid lesions based on the WHO 2022 criteria with an emphasis on the grey-zone lesions.

Follicular-patterned thyroid nodules (FPTN) are classified byWHO-2022 into benign, borderline and malignant categories. There are however, grey-zone lesions that pose a diagnostic challenge due to ambiguity in defining criteria and inter-observer variability. WHO-2022 has enumerated specific diagnostic criteria for these lesions. Accurate categorization of morphologically similar TNs is vital to reduce overtreatment of indolent lesions. In this study, we have reclassified FPTNs according to WHO-2022 criteria, emphasizing on grey-zone lesions. We studied the utility of immunohistochemistry (IHC)-CD56, HBME-1 and CK19 in distinguishing benign from malignant nodules and BRAFV600E IHC to better distinguish the (widely-invasive) encapsulated follicular variant of papillary thyroid carcinoma (FVPTC) from infiltrative FVPTC. Only those cases with dominant nodule having follicular pattern histology were included and re-evaluated for following histopathological features-focality, encapsulation, circumscription, nuclear PTC features, capsular-invasion, angio-invasion, papillae and necrosis. IHC findings for above-mentioned markers were noted. Seventy-nine cases met the inclusion criteria. Amendment of original diagnosis was done in 19 % cases. BRAFV600E IHC was positive in the two cases of infiltrative FVPTC while it was negative in all nine IE (invasive encapsulated) FVPTCs. Diffuse HBME1 was noted in most malignant nodules (61 %) while CD56 was expressed more often in benign lesions (70 %). CK19 was positive in lesions displaying nuclear PTC features (86 %). Using WHO 2022 criteria, we were able to re-classify follicular thyroid lesions with greater confidence. Appropriate IHC panel in adjunct to histology aids in categorizing challenging cases.

A risk stratification system developed to predict contralateral incidental malignant foci in early papillary thyroid carcinoma preoperatively.

BACKGROUND: In clinical practice, contralateral incidental malignant foci (CIMFs) can be found in some early (cT1N0M0) papillary thyroid carcinomas (PTCs) on postoperative pathological examination. To screen out the patients with high risk of CIMF preoperatively would help in determining the extent of thyroid surgery. METHODS: From October 2016 to February 2021, 332 patients diagnosed with early (cT1N0M0) PTC who underwent total thyroidectomy were included and randomly allocated into a training dataset (n = 233) and a test dataset (n = 99). Demographic and clinicopathological features were recorded and analyzed using logistic regression analysis. A coefficient-based nomogram was developed and validated. RESULTS: Logistic regression analyses revealed that the predictive model including BRAF V600E mutation, multifocality and margin of the contralateral nodule achieved the best diagnostic performance. The nomogram showed good discrimination, with AUCs of 0.795 (95 % CI, 0.736-0.853) for the training set and 0.726 (95 % CI, 0.609-0.843) for the test set. The calibration curve of the nomogram presented good agreement. CONCLUSION: The risk stratification system can be used to quantify the probability of CIMF and may assist in helping the patients choose total thyroidectomy or thyroid lobectomy with early (cT1N0M0) PTC.

Metabolic Profiles and Blood Biomarkers to Discriminate between Benign Thyroid Nodules and Papillary Carcinoma, Based on UHPLC-QTOF-ESI+-MS Analysis.

In this study, serum metabolic profiling of patients diagnosed with papillary thyroid carcinoma (PTC) and benign thyroid pathologies (BT) aimed to identify specific biomarkers and altered pathways when compared with healthy controls (C). The blood was collected after a histological confirmation from PTC (n = 24) and BT patients (n = 31) in parallel with healthy controls (n = 81). The untargeted metabolomics protocol was applied by UHPLC-QTOF-ESI+-MS analysis and the statistical analysis was performed using the MetaboAnalyst 5.0 platform. The partial least squares-discrimination analysis, including VIP values, random forest graphs, and heatmaps (p < 0.05), was complemented with biomarker analysis (with AUROC ranking) and pathway analysis, suggesting a model for abnormal metabolic pathways in PTC and BT based on 166 identified metabolites. There were 11 classes of putative biomarkers selected that were involved in altered metabolic pathways, e.g., polar molecules (amino acids and glycolysis metabolites, purines and pyrimidines, and selenium complexes) and lipids including free fatty acids, bile acids, acylated carnitines, corticosteroids, prostaglandins, and phospholipids. Specific biomarkers of discrimination were identified in each class of metabolites and upregulated or downregulated comparative to controls, PTC group, and BT group. The lipidomic window was revealed to be more relevant for finding biomarkers related to thyroid carcinoma or benign thyroid nodules, since our study reflected a stronger involvement of lipids and selenium-related molecules in metabolic discrimination.

Plasma miRNA-146b-3p, -222-3p, -221-5p, -21a-3p Expression Levels and TSHR Methylation: Diagnostic Potential and Association with Clinical and Pathological Features in Papillary Thyroid Cancer.

This study aimed to investigate the expression of microRNAs (miRNAs) -146b-3p, -221-5p, -222-3p, and -21a-3p and the methylation pattern of the thyroid-stimulating hormone receptor (TSHR) gene in blood plasma samples from papillary thyroid cancer (PTC) patients before and after thyroidectomy compared to healthy controls (HCs). This study included 103 participants, 46 PTC patients and 57 HCs, matched for gender and age. Significantly higher preoperative expression levels of miRNAs and TSHR methylation were determined in the PTC patients compared to HCs. Post-surgery, there was a notable decrease in these biomarkers. Elevated TSHR methylation was linked to larger tumor sizes and lymphovascular invasion, while increased miRNA-222-3p levels correlated with multifocality. Receiver operating characteristic (ROC) analysis showed AUCs below 0.8 for all candidate biomarkers. However, significant changes in the expression of all analyzed miRNAs and TSHR methylation levels indicate their potential to differentiate PTC patients from healthy individuals. These findings suggest that miRNAs and TSHR methylation levels may serve as candidate biomarkers for early diagnosis and monitoring of PTC, with the potential to distinguish PTC patients from healthy individuals. Further research is needed to validate these biomarkers for clinical application.