Discrimination of benign and malignant thyroid nodules by molecular profiling

Utility of the Growth Differentiation Factor-15 in the Differential Diagnosis of Follicular-Patterned Lesions of the Thyroid on Cytopathologic and Histopathologic Samples

Background Follicular-patterned lesions are a major gray zone in thyroid cytopathology. The recently introduced 2022 World Health Organization (WHO) classification emphasizes the importance of genetic alterations in thyroid neoplasms with the introduction of certain newer terminologies that are expected to cause remarkable changes in cytopathologic and histopathologic reporting. Although molecular assays such as the Afirma gene expression classifier and the ThyroSeq are already in use, there has been an ongoing search for further reliable molecular markers. The growth differentiation factor-15 (GDF-15) is one among them. This study aimed to determine the diagnostic utility of GDF-15 mRNA expression in frozen tissue and fine-needle aspiration (FNA) samples from follicular-patterned thyroid lesions and neoplasms. Methodology The real-time quantitative polymerase chain reaction was performed on 75 frozen tissue and FNA samples each from 19 cases of follicular thyroid hyperplasia (FTH), 10 nodular goiters (NGs), 17 follicular thyroid adenomas (FTAs), eight follicular thyroid carcinomas (FTCs), 12 follicular variant of papillary thyroid carcinomas (FVPTCs), and nine classic papillary thyroid carcinomas (CPTCs) that were diagnosed according to the 2017 WHO classification of thyroid neoplasms. The GDF-15 mRNA expression in all these cases was assessed and compared with the control thyroid tissue samples. One-way analysis of variance and the Kruskal-Wallis test were performed using GraphPad Prism 8 software to determine the significance of differences in the GDF-15 mRNA levels among various thyroid lesions. Results A higher GDF-15 mRNA expression was noted in the malignant thyroid neoplasms including FTC, FVPTC, and CPTC in comparison to FTA, with a fold change between the malignant and benign groups being more than 244.18 times. A difference in the fold change was noted between FTH and FTA with an increase in GDF-15 mRNA level in the latter, which was statistically not significant. Conclusions The fact that GDF-15 mRNA was studied both on fine-needle aspiration cytologic and the frozen tissue material and that the majority of the lesions studied were follicular-patterned establishes the GDF-15 as a potential marker not only for diagnosing malignant thyroid neoplasms of the follicular epithelium but also in distinguishing benign and malignant follicular-patterned neoplasms of the thyroid.

Differentiating Thyroid Follicular Adenoma from Follicular Carcinoma via G-Protein Coupled Receptor-Associated Sorting Protein 1 (GASP-1)

Follicular neoplasms are classified as benign or malignant depending on the presence or absence of capsular and/or vascular invasion. Due to incomplete capsular penetration or equivocal vascular invasion, the evaluation of these features can be challenging using histologic examination. In the current study, we analyzed the involvement of G-protein coupled receptor-associated sorting protein 1 (GASP-1) in the development and progression of thyroid neoplasms. Affinity-purified anti-GASP-1 polyclonal antibodies were used for routine immunohistochemistry (IHC) analysis. Thyroid tissue microarrays containing normal thyroid tissue, follicular adenoma, follicular carcinoma, papillary thyroid carcinoma, and anaplastic carcinoma were analyzed. We found that the level of GASP-1 expression can differentiate follicular adenoma from follicular carcinoma. When numerous cases were scored for GASP-1 expression by a board-certified pathologist, we found that GASP-1 expression is 7-fold higher in thyroid malignant neoplasms compared to normal thyroid tissue, and about 4-fold higher in follicular carcinoma compared to follicular adenoma. In follicular adenoma tissues, we observed the presence of many mini-glands that are enriched in GASP-1 and some mini-glands contain as few as three cells. GASP-1 IHC also possesses several advantages over the conventional H&E and can be used to identify early thyroid cancer and monitor cancer progression.

Evaluation of Diagnostic Utility of the Immunohistochemical Markers in the Accurate Diagnosis of Thyroid Neoplasms: A Retrospective Study in a Tertiary Care Hospital of Pakistan

Background

Thyroid cancer is the most common endocrine malignancy across the globe and is among the fastest-growing cancers worldwide. Thyroid tumors are divided into differentiated and non-differentiated, with each having further subtypes, with papillary carcinoma being the most common one. Immunohistochemical (IHC) markers’ studies play a crucial role in the accurate diagnosis of thyroid neoplasms. To the best of our knowledge, this topic has been the least researched in Pakistan.

Objectives

This study was designed to determine the diagnostic utility of immunohistochemical markers in the diagnosis of thyroid cancers in correlation with histopathology as the gold standard.

Methods

This retrospective, single-center study was carried out on 124 patients with thyroid cancer treated at our institution. The type of cancer, patient gender, and immunohistochemical markers used in each patient were recorded, and the sensitivity and specificity of the markers used in each tumor case were calculated.

Results

The mean age of patients was found to be 48.5 ± 15.6 years; 56 (45.2%) of the patients were male and 68 (54.8%) were female. Out of the 124 patients, 75 (60.5%) had papillary, 19 (15.3%) had medullary, 16 (12.9%) had anaplastic, and eight (6.5%) had follicular carcinoma, while six (4.8%) had primary thyroid lymphoma. Thyroglobulin was found to be a reliable tumor marker in both papillary and follicular tumors. The cluster of differentiation56 (CD56) negativity was a useful double panel study along with thyroglobulin in the confirmation of papillary carcinomas. Tumor markers used in medullary carcinoma include calcitonin, chromogranin, and synaptophysin. Cytokeratin AE 1 and vimentin were found to be useful for anaplastic tumors, while Ki 67 was a reliable marker for primary thyroid lymphoma.

Differentiation of follicular carcinomas from adenomas using histogram obtained from diffusion-weighted MRI

AIM

To evaluate the role of histogram analysis of apparent diffusion coefficient (ADC) maps from diffusion-weighted imaging (DWI) in the differentiation of follicular thyroid carcinoma (FTC) from follicular adenoma (FA) in nodules indeterminate on ultrasound-guided core needle biopsy (USCNB).

MATERIALS AND METHODS

This study was performed with institutional review board approval. Seventeen patients who were planned to undergo diagnostic lobectomy for an indeterminate thyroid nodule (atypical of unknown significance/follicular lesion of undetermined significance [AUS/FLUS] or suspicious for follicular neoplasm/follicular neoplasm [SFN]) on USCNB were enrolled prospectively. All patients underwent DWI on the day before surgery. Histogram parameters were derived from ADC values obtained from the whole extent of the tumours. The parameters were compared with the final diagnosis based on histopathological examination after surgery. The accuracy of the parameters in differentiating FTC from FA was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Twelve patients were confirmed as having FA and five patients as having FTC. Histogram parameters including the 10^th (ADC₁₀), 25^th (ADC₂₅), and 50^th (ADC₅₀) percentiles of the ADC values were significantly lower in FA than in FTC (p < 0.05, all). ROC curve analysis revealed that ADC₂₅ resulted in the highest AUC (0.867; confidence interval, 0.616-0.980), with a cut-off value of 0.352×10^-3 mm²/s.

CONCLUSION

Histogram parameters from ADC maps could differentiate FTC from FA effectively in indeterminate nodules on USCNB, with ADC₂₅ being the most promising parameter.

Noninvasive follicular neoplasm with papillary-like nuclear features (NIFTP): a 13-year retrospective review at Jordan University Hospital

OBJECTIVE

Noninvasive follicular neoplasm with papillary-like nuclear features (NIFTP) is the term to describe what was previously known as encapsulated follicular variant of papillary thyroid carcinoma. This new paradigm shift was agreed upon by experts in the field. The objective of this study is to evaluate cases previously diagnosed as follicular adenomas, follicular variant of papillary thyroid carcinoma and hyperplastic nodules to be reclassified as NIFTP according to the new criteria. Furthermore, the clinical follow-up of these NIFTP cases is evaluated.

METHODS

This retrospective study reviewed potential NIFTP cases over the last 13 years, at Jordan University Hospital.

RESULTS

A total of 811 thyroid surgery reports were identified and revised to identify the potential NIFTP cases. The review yielded 173 cases identified as potential NIFTP cases. Further pathological slide review resulted in a revised diagnosis of 32 cases of NIFTP according to the new criteria. The NIFTP cases comprised 4% of the total number of thyroidectomy cases and 16.1% of the total pool of previously diagnosed papillary thyroid carcinoma cases at our institution. While 111 cases retained their original diagnosis. Follow-up showed that all patients are alive and well with no evidence of disease.

CONCLUSION

Patients with NIFTP are not uncommon and the diagnosis is made only after a thorough evaluation of excision. Therefore, initial conservative management of solitary thyroid nodules suspicious for NIFTP in the form of lobectomy is recommended to avoid unnecessary total thyroidectomies. Our follow-up of NIFTP cases is similar to all previous reports.

In Silico Integration Approach Reveals Key MicroRNAs and Their Target Genes in Follicular Thyroid Carcinoma

To better understand the molecular mechanism for the pathogenesis of follicular thyroid carcinoma (FTC), this study aimed at identifying key miRNAs and their target genes associated with FTC, as well as analyzing their interactions. Based on the gene microarray data GSE82208 and microRNA dataset GSE62054, the differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained using R and SAM software. The common DEMs from R and SAM were fed to three different bioinformatic tools, TargetScan, miRDB, and miRTarBase, respectively, to predict their biological targets. With DEGs intersected with target genes of DEMs, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed through the DAVID database. Then a protein-protein interaction (PPI) network was constructed by STRING. Finally, the module analysis for PPI network was performed by MCODE and BiNGO. A total of nine DEMs were identified, and their function might work through regulating hub genes in the PPI network especially KIT and EGFR. KEGG analysis showed that intersection genes were enriched in the PI3K-Akt signaling pathway and microRNAs in cancer. In conclusion, the study of miRNA-mRNA network would offer molecular support for differential diagnosis between malignant FTC and benign FTA, providing new insights into the potential targets for follicular thyroid carcinoma diagnosis and treatment.

Molecular testing in the diagnosis of differentiated thyroid carcinomas

Different genetic mutations and other molecular alterations in papillary thyroid cancer (PTC) and follicular thyroid cancer (FTC) can be detected in fine-needle aspiration (FNA) of thyroid nodules, and can be used successfully to ameliorate cancer diagnosis and management of patients with thyroid nodules. The greatest experience has been obtained with the diagnostic use of BRAF mutation that is strongly specific for malignancy when detected using well-validated techniques. The strongest diagnostic result can be obtained testing FNA samples for a panel of mutations that typically involve TERT, BRAF, PAX8/PPARγ, RAS, and RET/PTC. Finding any of these mutations in a thyroid nodule provides strong indication for malignancy and helps to refine clinical management for a significant proportion of patients with indeterminate cytology. The use of molecular markers, as TERT, BRAF, PAX8/PPARγ, RAS, and RET/PTC, may be considered for patients with indeterminate FNA cytology (FNAC) to help guide management. In patients with indeterminate TIR3 FNA, the combination of precise molecular marker expression analysis with molecular mutations evaluations could ameliorate significantly the accuracy of cancer diagnosis. However other prospective studies are needed to identify more accurate molecular markers. Finally, the knowledge of these molecular pathways has permitted the development of new targeted therapies for aggressive TC.

Thyroid follicular adenomas and carcinomas: molecular profiling provides evidence for a continuous evolution

Non-autonomous thyroid nodules are common in the general population with a proportion found to be cancerous. A current challenge in the field is to be able to distinguish benign adenoma (FA) from preoperatively malignant thyroid follicular carcinoma (FTC), which are very similar both histologically and genetically. One controversial issue, which is currently not understood, is whether both tumor types represent different molecular entities or rather a biological continuum. To gain a better insight into FA and FTC tumorigenesis, we defined their molecular profiles by mRNA and miRNA microarray. Expression data were analyzed, validated by qRT-PCR and compared with previously published data sets. The majority of deregulated mRNAs were common between FA and FTC and were downregulated, however FTC showed additional deregulated mRNA. Both types of tumors share deregulated pathways, molecular functions and biological processes. The additional deregulations in FTC include the lipid transport process that may be involved in tumor progression. The strongest candidate genes which may be able to discriminate follicular adenomas and carcinomas, CRABP1, FABP4 and HMGA2, were validated in independent samples by qRT-PCR and immunohistochemistry. However, they were not able to adequately classify FA or FTC, supporting the notion of continuous evolving tumors, whereby FA and FTC appear to show quantitative rather than qualitative changes. Conversely, miRNA expression profiles showed few dysregulations in FTC, and even fewer in FA, suggesting that miRNA play a minor, if any, role in tumor progression.

A six-gene panel to label follicular adenoma, low- and high-risk follicular thyroid carcinoma

The distinction between follicular thyroid carcinomas (FTCs) and follicular-patterned benign lesions is almost impossible on fine-needle aspiration cytology. Furthermore, minimally invasive FTCs (MI-FTCs) with less than 4 vascular invasion foci generally have an excellent prognosis, but there are exceptions and, so far, no molecular marker appears able to identify them reliably. We aimed to distinguish benign lesions from low- and high-risk FTCs by a small-scale combination of genes. The expression analysis of 75 selected genes was performed on 18 follicular adenomas (FAs), 14 MI-FTCs and 6 widely invasive FTC (WI-FTCs). The mutational status of the RAS genes, TERT promoter and PAX8-PPARG rearrangements was also investigated. Seven samples were mutated, namely 3 MI-FTCs and 4 WI-FTCs. Twenty-five genes were differentially expressed (FDR <0.05) between FAs and WI-FTCs. Six of these (ECM1, RXRG, SDPR, SLC26A4, TIFF3, TIMP1) were also differently expressed among MI-FTCs and FAs or WI-FTCs and were considered to build a classification model, which was tested to classify samples according to their histological class. Hence, 31 out of 38 were correctly classified, and accuracy remained high after cross-validation (27/38). The 2 MI-FTCs incorrectly classified as WI-FTCs harbored both RAS and TERT promoter mutations. The capability of these six genes to stratify benign, low- and high-risk lesions appears to be promising in supporting the diagnosis of indeterminate thyroid nodules.

Overexpression of teneurin transmembrane protein 1 is a potential marker of disease progression in papillary thyroid carcinoma

Although papillary thyroid cancer is a relatively indolent malignancy, its progression may be associated with dedifferentiation and resistance to radioactive iodine treatment. In this study, patterns of differentially expressed genes in association with disease progression were systemically evaluated. We firstly performed transcriptome analyses for four matched cancerous and noncancerous tissue pairs of the classical subtype of papillary thyroid cancer. Among the upregulated and downregulated genes, the expression of 164 and 183 genes increased and decreased, respectively, from stage I to stage IV. Functional enrichment and pathway analysis showed that angiogenesis pathway was upregulated, whereas oxidation-reduction and metabolism of reactive oxygen species were downregulated. Teneurin transmembrane protein 1 (TENM1) expression was highly upregulated in cancerous tissues and negative in benign thyroid tissues. By immunohistochemistry, TENM1 expression in papillary thyroid cancer was associated with the classical subtype (p = 0.018), extrathyroidal invasion (p = 0.001), BRAF V600E mutation (p < 0.001), and an advanced stage (p = 0.019). Taken together, our results indicate that distinct pathways are involved in papillary thyroid cancer progression, and TENM1 is a potential marker of cancer progression.

Integrated microRNA-mRNA analyses of distinct expression profiles in follicular thyroid tumors

MicroRNAs (miRNAs/miRs) are small non-coding RNAs identified in plants, animals and certain viruses; they function in RNA silencing and post-transcriptional regulation of gene expression. miRNAs also serve an important role in the pathogenesis, diagnosis and treatment of tumors. However, few studies have investigated the role of miRNAs in thyroid tumors. In the present study, the expression of miRNA and mRNA was compared between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FA) samples, and then miRNA-mRNA regulatory network analysis was performed. Microarray datasets (GSE29315 and GSE62054) were downloaded from the Gene Expression Omnibus, and profiling data were processed with R software. Differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were determined, and Gene Ontology enrichment analysis was subsequently performed for DEGs using the Database for Annotation, Visualization and Integrated Discovery. The target genes of the DEMs were identified with miRWalk, miRecords and TarMir databases. Network analysis of the DEMs and DEMs-targeted DEGs was performed using Cytoscape software. In GSE62054, 23 downregulated and 9 upregulated miRNAs were identified. In GSE29315, 42 downregulated and 44 upregulated mRNAs were identified. A total of 36 miRNA-gene pairs were also identified. Network analysis indicated a co-regulatory association between miR-296-5p, miR-10a, miR-139-5p, miR-452, miR-493, miR-7, miR-137, miR-144, miR-145 and corresponding targeted mRNAs, including TNF receptor superfamily member 11b, benzodiazepine receptor (peripheral) -associated protein 1, and transforming growth factor β receptor 2. These results suggest that miRNA-mRNAs networks serve an important role in the pathogenesis, diagnosis and treatment of FTC and FA.

Identification of Two Distinct Molecular Subtypes of Non-Invasive Follicular Neoplasm with Papillary-Like Nuclear Features by Digital RNA Counting

BACKGROUND

The follicular variant (FV) of papillary thyroid cancer (PTC) is one of the most common variants of PTC. Clinically, non-infiltrative FVPTC is considered a low-risk variant of PTC, and the non-invasive encapsulated forms of FVPTC represent a group of thyroid tumors with a particularly good prognosis. Consequently, these neoplasms have been very recently reclassified as non-invasive follicular neoplasms with papillary-like nuclear features (NIFTP). From a molecular standpoint, NIFTP appears to be similar to follicular neoplasms. However, only limited data are currently available regarding their gene expression profile.

METHODS

The aim of this study was to identify specific molecular signatures of 26 NIFTPs compared to those of 19 follicular adenomas (FAs) and 18 infiltrative FVPTCs (IFVPTCs). A nanoString custom assay was used to perform mRNA expression analysis. All cases were also genotyped for BRAF, N-, H-, and K-RAS mutations. Samples were grouped on the basis of gene expression profiles by Pearson's correlation and non-negative matrix factorization clustering analysis. Finally, the uncorrelated shrunken centroid machine-learning algorithm was used to classify the samples.

RESULTS

The results revealed distinct expression profiles of FAs and IFVPTCs. NIFTP samples can exhibit different expression profiles, more similar to FAs (FA-like) or to IFVPTCs (IFVPTC-like), and these different expression profiles largely depend on the presence of different mutations (RAS or BRAF).

CONCLUSION

In conclusion, although further validation of the model is required by using a larger group of prospective cases, these data reinforce the hypothesis that IFVPTC-like NIFTPs might represent precursors of IFVPTC.

Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach

BACKGROUND AND OBJECTIVES

It is important to be able to accurately distinguish between benign and malignant thyroid nodules in order to make appropriate clinical decisions. The purpose of this study was to improve the effectiveness and efficiency for discriminating the malignant from benign thyroid cancers based on the Ultrasonography (US) features.

METHODS

There were 114 benign nodules in 106 patients (82 women and 24 men) and 89 malignant nodules in 81 patients (69 women and 12 men) included in this study. The potential of extreme learning machine (ELM) has been explored for the first time to discriminate malignant and benign thyroid nodules based on the sonographic features in ultrasound images. The influence of two key parameters (the number of hidden neurons and type of activation function) on the performance of ELM was investigated. The relationship between feature subsets obtained by the feature selection method and the classification performance of ELM was also examined. A real-life dataset was used to evaluate the effectiveness of the proposed method in terms of classification accuracy, sensitivity, specificity, and area under the ROC (receiver operating characteristic) curve (AUC).

RESULTS

The results demonstrate that there are significant differences between the malignant and benign thyroid nodules (p-value<0.01), the most discriminative features are echogenicity, calcification, margin, composition and shape. Compared with other methods, the proposed method not only has achieved very promising classification accuracy via 10-fold cross-validation (CV) scheme, but also greatly reduced the computational cost compared to other counterparts. The proposed ELM-based approach achieves 87.72% ACC, 0.8672 AUC, 78.89% sensitivity, and 94.55% specificity.

CONCLUSIONS

Based on the empirical analysis, the proposed ELM-based approach for thyroid cancer detection has promising potential in clinical use, and it can be of assistance as an optional tool for the clinicians.

C Cell and Follicular Epithelial Cell Precursor Lesions of the Thyroid

CONTEXT

- The identification of precursor or dysplastic lesions in the thyroid is difficult. Pathology of the C cell has been extensively studied, and the preneoplastic nature of C-cell hyperplasia in the setting of familial medullary thyroid carcinomas is well established. However, the distinction between neoplastic and physiologic/reactive C-cell hyperplasia remains a challenge. Unlike C cells, the existence of a precursor lesion of follicular cell-derived tumors is less well established, and a dysplastic or preneoplastic follicular lesion has not been well defined.

OBJECTIVE

- To discuss putative precursor lesions in the thyroid arising from C cells and follicular epithelial cells.

DATA SOURCES

- Data were obtained from a review of the pertinent peer-reviewed literature.

CONCLUSIONS

- Although the preneoplastic nature of C-cell hyperplasia in the setting of familial medullary thyroid carcinoma is well recognized, the preneoplastic nature/malignant potential of reactive/physiologic C-cell hyperplasia and its role in the development of sporadic, medullary thyroid carcinoma is still unclear. Current data suggest that benign follicular lesions may have malignant potential, and there may be a multifocal progression from benign to malignant. Atypical follicular lesions in the background of chronic lymphocytic thyroiditis may represent dysplastic or premalignant lesions.

Surgical treatment of thyroid follicular neoplasms: results of a retrospective analysis of a large clinical series

The most appropriate surgical management of "follicular neoplasm/suspicious for follicular neoplasm" lesions (FN), considering their low definitive malignancy rate and the limited predictive power of preoperative clinic-diagnostic factors, is still controversial. On behalf of the Italian Association of Endocrine Surgery Units (U.E.C. CLUB), we collected and analyzed the experience of 26 endocrine centers by computerized questionnaire. 1379 patients, surgically treated after a FN diagnosis from January 2012 and December 2103, were evaluated. Histological features, surgical complications, and medium-term outcomes were reported. Total thyroidectomy (TT) was performed in 1055/1379 patients (76.5 %), while hemithyroidectomy (HT) was carried out in 324/1379 cases (23.5 %). Malignancy rate was higher in TT than in HT groups (36.4 vs. 26.2 %), whereas the rates of transient and definitive hypoparathyroidism following TT were higher than after HT. Consensual thyroiditis (16.8 vs. 9.9 %) and patient age (50.9 vs. 47.9 %) also differed between groups. A cytological FN diagnosis was associated to a not negligible malignancy rate (469/1379 patients; 34 %), that was higher in TT than in HT groups. However, a lower morbidity rate was observed in HT, which should be considered the standard of care in solitary lesions in absence of specific risk factors. Malignancy could not be preoperatively assessed and clinical decision-making is still controversial. Further efforts should be spent to more accurately preoperatively classify FN thyroid nodules.

Improving the Accuracy of Early Diagnosis of Thyroid Nodule Type Based on the SCAD Method

Although early diagnosis of thyroid nodule type is very important, the diagnostic accuracy of standard tests is a challenging issue. We here aimed to find an optimal combination of factors to improve diagnostic accuracy for distinguishing malignant from benign thyroid nodules before surgery. In a prospective study from 2008 to 2012, 345 patients referred for thyroidectomy were enrolled. The sample size was split into a training set and testing set as a ratio of 7:3. The former was used for estimation and variable selection and obtaining a linear combination of factors. We utilized smoothly clipped absolute deviation (SCAD) logistic regression to achieve the sparse optimal combination of factors. To evaluate the performance of the estimated model in the testing set, a receiver operating characteristic (ROC) curve was utilized. The mean age of the examined patients (66 male and 279 female) was 40.9 ± 13.4 years (range 15- 90 years). Some 54.8% of the patients (24.3% male and 75.7% female) had benign and 45.2% (14% male and 86% female) malignant thyroid nodules. In addition to maximum diameters of nodules and lobes, their volumes were considered as related factors for malignancy prediction (a total of 16 factors). However, the SCAD method estimated the coefficients of 8 factors to be zero and eliminated them from the model. Hence a sparse model which combined the effects of 8 factors to distinguish malignant from benign thyroid nodules was generated. An optimal cut off point of the ROC curve for our estimated model was obtained (p=0.44) and the area under the curve (AUC) was equal to 77% (95% CI: 68%-85%). Sensitivity, specificity, positive predictive value and negative predictive values for this model were 70%, 72%, 71% and 76%, respectively. An increase of 10 percent and a greater accuracy rate in early diagnosis of thyroid nodule type by statistical methods (SCAD and ANN methods) compared with the results of FNA testing revealed that the statistical modeling methods are helpful in disease diagnosis. In addition, the factor ranking offered by these methods is valuable in the clinical context.

Association between FOXP3 gene polymorphisms and risk of differentiated thyroid cancer in Chinese Han population

BACKGROUND

Foxp3 plays important roles in autoimmune and inflammatory diseases as well as human malignancies. This study aimed to investigate the association between Foxp3 gene polymorphisms and the susceptibility to differentiated thyroid cancers (DTC).

METHODS

Genotyping was performed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in 350 DTC patients and 306 healthy controls. FOXP3 relative expression was measured by real-time quantitative PCR (qRT-PCR).

RESULTS

AA/AC genotype of Foxp3-rs3761548 was associated with a higher risk of DTC. The frequency of Foxp3-rs2280883 CC/CT genotype was lower in DTC patients. Besides, the AA/AC genotype of rs3761548 was more frequent in female DTC than male DTC. The association between two single nucleotide polymorphisms (SNPs) and clinical characteristics of DTC was further analyzed. We found that rs3761548 AA/AC genotype was more frequent in severe DTC patients (tumor diameter >1 cm) compared with the relative tender DTC patients (tumor diameter <1 cm). On the contrast, the frequency of rs2280883 CC/CT genotype was lower in severe DTC patients. In addition, the Foxp3 relative expression in DTC with AA/AC genotype of rs3761548 was higher than that of DTC with CC genotype.

CONCLUSION

Our findings suggested that Foxp3 polymorphisms were associated with the risk of DTC in Chinese Han population.

Classification of Benign and Malignant Thyroid Nodules Using a Combined Clinical Information and Gene Expression Signatures

BACKGROUND

A key challenge in thyroid carcinoma is preoperatively diagnosing malignant thyroid nodules. A novel diagnostic test that measures the expression of a 3-gene signature (DPP4, SCG5 and CA12) has demonstrated promise in thyroid carcinoma assessment. However, more reliable prediction methods combining clinical features with genomic signatures with high accuracy, good stability and low cost are needed.

METHODOLOGY/PRINCIPAL FINDINGS

25 clinical information were recorded in 771 patients. Feature selection and validation were conducted using random forest. Thyroid samples and clinical data were obtained from 142 patients at two different hospitals, and expression of the 3-gene signature was measured using quantitative PCR. The predictive abilities of three models (based on the selected clinical variables, the gene expression profile, and integrated gene expression and clinical information) were compared. Seven clinical characteristics were selected based on a training set (539 patients) and tested in three test sets, yielding predictive accuracies of 82.3% (n = 232), 81.4% (n = 70), and 81.9% (n = 72). The predictive sensitivity, specificity, and accuracy were 72.3%, 80.5% and 76.8% for the model based on the gene expression signature, 66.2%, 81.8% and 74.6% for the model based on the clinical data, and 83.1%, 84.4% and 83.8% for the combined model in a 10-fold cross-validation (n = 142).

CONCLUSIONS

These findings reveal that the integrated model, which combines clinical data with the 3-gene signature, is superior to models based on gene expression or clinical data alone. The integrated model appears to be a reliable tool for the preoperative diagnosis of thyroid tumors.

Role of gene expression profiling in defining indeterminate thyroid nodules in addition to BRAF analysis

Fine-needle aspiration (FNA) is routinely used in the preoperative evaluation of thyroid nodules. However, 15% to 30% of aspirations yield indeterminate cytologic findings. Because the assessment of BRAF mutations seems to improve the diagnostic accuracy, this study evaluated BRAF mutations with Sanger sequencing and real-time methods in 650 consecutive thyroid aspirates. In addition, the expression of a large number of genes involved in basement membrane remodeling, extracellular matrix proteolysis, and cell adhesion was studied in both benign and malignant nodules to identify new diagnostic tools. In this prospective series, despite the use of a very sensitive BRAF mutational testing method, the frequency of a BRAF alteration being identified in indeterminate FNA samples was 3 of 68. Expression analysis revealed several genes that were differentially expressed between benign and malignant nodules (transforming growth factor, cadherin 1, collagen α1, catenin α1, integrin α3, and fibronectin 1 [FN1]), between follicular adenomas and follicular variant of papillary thyroid carcinoma (FN1, laminin γ1, integrin β2, connective tissue growth factor, catenin δ1, and integrin αV), and between BRAF-wild-type and BRAF-mutated papillary thyroid carcinomas (TIMP metallopeptidase inhibitor 1; catenin α1; secreted phosphoprotein 1; FN1; ADAM metallopeptidase with thrombospondin type 1 motif, 1; and selectin L). These data were partially confirmed with real-time polymerase chain reaction analysis and immunohistochemistry. When the cost/benefit ratio of the procedures was taken into account, BRAF mutational testing failed to increase diagnostic accuracy in cytologically indeterminate nodules. However, the additional analysis of the expression of specific molecular markers could have possible utility as a diagnostic tool, although further evidence based on a large series of samples is needed before definitive conclusions can be drawn. Cancer Cytopathol 2016;124:340-9. © 2015 American Cancer Society.

A Model Using Texture Features to Differentiate the Nature of Thyroid Nodules on Sonography

OBJECTIVES

To evaluate the use of texture-based gray-level co-occurrence matrix (GLCM) features extracted from thyroid sonograms in building prediction models to determine the nature of thyroid nodules.

METHODS

A GLCM was used to extract the texture features of 155 sonograms of thyroid nodules (76 benign and 79 malignant). The GLCM features included energy, contrast, correlation, sum of squares, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, information measures of correlation, and maximal correlation coefficient. The texture features extracted by the GLCM were used to build 6 different statistical models, including support vector machine, random tree, random forest, boost, logistic, and artificial neural network models. The models' performances were evaluated by 10-fold cross-validation combining a receiver operating characteristic curve, indices of accuracy, true-positive rate, false-positive rate, sensitivity, specificity, precision, recall, F-measure, and area under the receiver operating characteristic curve. External validation was used to examine the stability of the model that showed the best performance.

RESULTS

The logistic model showed the best performance, according to 10-fold cross-validation, among the 6 models, with the highest area under the curve (0.84), accuracy (78.5%), true-positive rate (0.785), sensitivity (0.789), specificity (0.785), precision (0.789), recall (0.785), and F-measure (0.784), as well as the lowest false-positive rate (0.215). The external validation results showed that the logistic model was stable.

CONCLUSIONS

Gray-level co-occurrence matrix texture features extracted from sonograms of thyroid nodules coupled with a logistic model are useful for differentiating between benign and malignant thyroid nodules.

Effect of BRAF mutational status on expression profiles in conventional papillary thyroid carcinomas

BACKGROUND

Whereas 40 % to 70 % of papillary thyroid carcinomas (PTCs) are characterized by a BRAF mutation (BRAFmut), unified biomarkers for the genetically heterogeneous group of BRAF wild type (BRAFwt) PTCs are not established yet. Using state-of-the-art technology we compared RNA expression profiles between conventional BRAFwt and BRAFmut PTCs.

METHODS

Microarrays covering 36,079 reference sequences were used to generate whole transcript expression profiles in 11 BRAFwt PTCs including five micro PTCs, 14 BRAFmut PTCs, and 7 normal thyroid specimens. A p-value with a false discovery rate (FDR) < 0.05 and a fold change > 2 were used as a threshold of significance for differential expression. Network and pathway utilities were employed to interpret significance of expression data. BRAF mutational status was established by direct sequencing the hotspot region of exon 15.

RESULTS

We identified 237 annotated genes that were significantly differentially expressed between BRAFwt and BRAFmut PTCs. Of these, 110 genes were down- and 127 were upregulated in BRAFwt compared to BRAFmut PTCs. A number of molecules involved in thyroid hormone metabolism including thyroid peroxidase (TPO) were differentially expressed between both groups. Among cancer-associated molecules were ERBB3 that was downregulated and ERBB4 that was upregulated in BRAFwt PTCs. Two microRNAs were significantly differentially expressed of which miR492 bears predicted functions relevant to thyroid-specific molecules. The protein kinase A (PKA) and the G protein-coupled receptor pathways were identified as significantly related signaling cascades to the gene set of 237 genes. Furthermore, a network of interacting molecules was predicted on basis of the differentially expressed gene set.

CONCLUSIONS

The expression study focusing on affected genes that are differentially expressed between BRAFwt and BRAFmut conventional PTCs identified a number of molecules which are connected in a network and affect important canonical pathways. The identified gene set adds to our understanding of the tumor biology of BRAFwt and BRAFmut PTCs and contains genes/biomarkers of interest.

Comparison of microarray expression profiles between follicular variant of papillary thyroid carcinomas and follicular adenomas of the thyroid

BACKGROUND

Follicular variant of papillary thyroid carcinoma (FVPTC) and follicular adenoma (FA) are histologically closely related tumors and differential diagnosis remains challenging. RNA expression profiling is an established method to unravel molecular mechanisms underlying the histopathology of diseases.

METHODS

BRAF mutational status was established by direct sequencing the hotspot region of exon 15 in six FVPTCs and seven FAs. Whole-transcript arrays were employed to generate expression profiles in six FVPTCs, seven FAs and seven normal thyroid tissue samples. The threshold of significance for differential expression on the gene and exon level was a p-value with a false discovery rate (FDR) < 0.05 and a fold change cutoff > 2. Two dimensional average linkage hierarchical clustering was generated using differentially expressed genes. Network, pathway, and alternative splicing utilities were employed to interpret significance of expression data on the gene and exon level.

RESULTS

Expression profiling in FVPTCs and FAs, all of which were negative for a BRAF mutation, revealed 55 transcripts that were significantly differentially expressed, 40 of which were upregulated and 15 downregulated in FVPTCs vs. FAs. Amongst the most significantly upregulated genes in FVPTCs were GABA B receptor, 2 (GABBR2), neuronal cell adhesion molecule (NRCAM), extracellular matrix protein 1 (ECM1), heparan sulfate 6-O-sulfotransferase 2 (HS6ST2), and retinoid X receptor, gamma (RXRG). The most significantly downregulated genes in FVPTCs included interaction protein for cytohesin exchange factors 1 (IPCEF1), G protein-coupled receptor 155 (GPR155), Purkinje cell protein 4 (PCP4), chondroitin sulfate N-acetylgalactosaminyltransferase 1 (CSGALNACT1), and glutamate receptor interacting protein 1 (GRIP1). Alternative splicing analysis detected 87 genes, 52 of which were also included in the list of 55 differentially expressed genes. Network analysis demonstrated multiple interactions for a number of differentially expressed molecules including vitamin D (1,25- dihydroxyvitamin D3) receptor (VDR), SMAD family member 9 (SMAD9), v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT), and RXRG.

CONCLUSIONS

This is one of the first studies using whole-transcript expression arrays to compare expression profiles between FVPTCs and FAs. A set of differentially expressed genes has been identified that contains valuable candidate genes to differentiate both histopathologically related tumor types on the molecular level.

Prevalence of c.2268dup and detection of two novel alterations, c.670_672del and c.1186C>T, in the TPO gene in a cohort of Malaysian-Chinese with thyroid dyshormonogenesis

OBJECTIVES

The c.2268dup mutation in the thyroid peroxidase (TPO) gene is the most common TPO alteration reported in Taiwanese patients with thyroid dyshormonogenesis. The ancestors of these patients are believed to originate from the southern province of China. Our previous study showed that this mutation leads to reduced abundance of the TPO protein and loss of TPO enzyme activity in a Malaysian-Chinese family with goitrous hypothyroidism. The aim of our study was to provide further data on the incidence of the c.2268dup mutation in a cohort of Malaysian-Chinese and its possible phenotypic effects.

SETTING

Cohort study.

PARTICIPANTS

Twelve biologically unrelated Malaysian-Chinese patients with congenital hypothyroidism were recruited in this study. All patients showed high thyrotropin and low free thyroxine levels at the time of diagnosis with proven presence of a thyroid gland.

PRIMARY OUTCOME MEASURE

Screening of the c.2268dup mutation in the TPO gene in all patients was carried out using a PCR-direct DNA sequencing method.

SECONDARY OUTCOME MEASURE

Further screening for mutations in other exonic regions of the TPO gene was carried out if the patient was a carrier of the c.2268dup mutation.

RESULTS

The c.2268dup mutation was detected in 4 of the 12 patients. Apart from the c.2268dup and a previously documented mutation (c.2647C>T), two novel TPO alterations, c.670_672del and c.1186C>T, were also detected in our patients. In silico analyses predicted that the novel alterations affect the structure/function of the TPO protein.

CONCLUSIONS

The c.2268dup mutation was detected in approximately one-third of the Malaysian-Chinese patients with thyroid dyshormonogenesis. The detection of the novel c.670_672del and c.1186C>T alterations expand the mutation spectrum of TPO associated with thyroid dyshormonogenesis.

A three-gene panel that distinguishes benign from malignant thyroid nodules

Reliable preoperative diagnosis of malignant thyroid tumors remains challenging because of the inconclusive cytological examination of fine-needle aspiration biopsies. Although numerous studies have successfully demonstrated the use of high-throughput molecular diagnostics in cancer prediction, the application of microarrays in routine clinical use remains limited. Our aim was, therefore, to identify a small subset of genes to develop a practical and inexpensive diagnostic tool for clinical use. We developed a two-step feature selection method composed of a linear models for microarray data (LIMMA) linear model and an iterative Bayesian model averaging model to identify a suitable gene set signature. Using one public dataset for training, we discovered a three-gene signature dipeptidyl-peptidase 4 (DPP4), secretogranin V (SCG5) and carbonic anhydrase XII (CA12). We then evaluated the robustness of our gene set using three other independent public datasets. The gene signature accuracy was 85.7, 78.8 and 85.7%, respectively. For experimental validation, we collected 70 thyroid samples from surgery and our three-gene signature method achieved an accuracy of 94.3% by quantitative polymerase chain reaction (QPCR) experiment. Furthermore, immunohistochemistry in 29 samples showed proteins expressed by these three genes are also differentially expressed in thyroid samples. Our protocol discovered a robust three-gene signature that can distinguish benign from malignant thyroid tumors, which will have daily clinical application.

A Review on Ultrasound-Based Thyroid Cancer Tissue Characterization and Automated Classification

In this paper, we review the different studies that developed Computer Aided Diagnostic (CAD) for automated classification of thyroid cancer into benign and malignant types. Specifically, we discuss the different types of features that are used to study and analyze the differences between benign and malignant thyroid nodules. These features can be broadly categorized into (a) the sonographic features from the ultrasound images, and (b) the non-clinical features extracted from the ultrasound images using statistical and data mining techniques. We also present a brief description of the commonly used classifiers in ultrasound based CAD systems. We then review the studies that used features based on the ultrasound images for thyroid nodule classification and highlight the limitations of such studies. We also discuss and review the techniques used in studies that used the non-clinical features for thyroid nodule classification and report the classification accuracies obtained in these studies.

Immunohistochemical expression of HBME-1 and galectin-3 in the differential diagnosis of follicular-derived thyroid nodules

BACKGROUND

Thyroid nodules are common among adults with only a small percentage being malignant and histologically mimic benign nodules. Accurate diagnosis of these thyroid nodules is critical for the proper clinical management. The determination of malignancy in follicular patterned thyroid lesions is based on postoperative histological findings. Therefore, affected patients are referred for surgery, although only 10% will have a final diagnosis of malignancy. The aim of this study was to investigate the ability of two immunohistochemical (IHC) markers; galectin-3 and Hector Battifora mesothelial-1 (HBME-1) individually or in combination, to distinguish between benign (non-neoplastic and neoplastic) and malignant (follicular and papillary carcinomas) thyroid lesions removed by surgical resection.

METHODS

We investigated the immunoexpression of galectin-3 and HBME-1 in 50 cases of benign and malignant thyroid nodules. The benign group included 13 cases of thyroid nodular goiter (NG) and 9 cases of follicular adenoma (FA). The malignant group included 5 cases of follicular thyroid carcinomas (FC), 18 cases of classic papillary thyroid carcinoma and 5 cases of follicular variant papillary carcinoma (FVPC).

RESULTS

The staining results showed that malignant tumors expressed galectin-3 and HBME-1 significantly more than benign nodules. The sensitivity of these markers for the distinction between benign and malignant lesions ranged from 89.3% to 92.9%. Co-expression of galectin-3 and HBME-1 was seen in 82.1% of carcinomas, but in none of the benign nodules. Immunoexpression was usually diffuse in malignant tumors, and focal in the benign lesions.

CONCLUSION

Our findings indicate that these immunohistochemical markers are significantly more expressed in malignant tumors compared to benign lesions and may be of additional diagnostic value when combined with routine histology. Galectin-3 has higher sensitivity and specificity of immunoexpression in thyroid malignancy than HBME-1, and the combined use of galectin-3 and HBME-1 can increase the specificity of immunoexpression in malignant tumors.

Controversies in the surgical management of thyroid follicular neoplasms. Retrospective analysis of 721 patients

The most appropriate surgical management of "follicular neoplasm/suspicious for follicular neoplasm" lesions, is still controversial. Analysing and comparing the experience of two units for endocrine surgery, we retrospectively evaluated 721 patients, surgically treated after a follicular neoplasm diagnosis. Total thyroidectomy was routinely performed in one Institution, while in the other one it was selectively carried out. The main criteria leading to hemythyroidectomy were a single nodule, the age ≤45 years, the absence of thyroiditis or clinical/intraoperative suspicion of malignancy. Total thyroidectomy was performed in 402/721 patients (55.7%), hemythyroidectomy in 319/721 cases (44.2%) and a completion thyroidectomy in 51/319 cases (15.9%). The overall malignancy rate was 24% (176/721 patients), respectively 16% (51/319 patients) following hemythyroidectomy, and 31% (125/402 patients) following total thyroidectomy. Definitive recurrent laryngeal nerve paralysis and permanent hypoparathyroidism were not reported in hemythyroidectomy patients in which lower mean hospitalization and costs were observed. Considering the low-risk of follicular neoplasm solitary lesions, hemythyroidectomy is still the safest standard of care with lower hospitalization and costs. In case of multiglandular disease or thyroiditis, that might be associated with a higher risk of cancer, total thyroidectomy should be recommended. Further investigation is warranted to achieve a better preoperative follicular neoplasm diagnostic accuracy in order to reduce the amount of unnecessary surgical operations with a diagnostic aim.

Carbonic anhydrase 4 and crystallin α-B immunoreactivity may distinguish benign from malignant thyroid nodules in patients with indeterminate thyroid cytology

BACKGROUND

Thyroid nodules are present in 19%-67% of the population and carry a 5%-10% risk of malignancy. Unfortunately, fine-needle aspiration biopsies are indeterminate in 20%-30% of patients, often necessitating thyroid surgery for diagnosis. Numerous DNA microarray studies including a recently commercialized molecular classifier have helped to better distinguish benign from malignant thyroid nodules. Unfortunately, these assays often require probes for >100 genes, are expensive, and only available at a few laboratories. We sought to validate these DNA microarray assays at the protein level and determine whether simple and widely available immunohistochemical biomarkers alone could distinguish benign from malignant thyroid nodules.

METHODS

A tissue microarray (TMA) composed of 26 follicular thyroid carcinomas (FTCs) and 53 follicular adenomas (FAs) from patients with indeterminate thyroid nodules was stained with 17 immunohistochemical biomarkers selected based on prior DNA microarray studies. Antibodies used included galectin 3, growth and differentiation factor 15, protein convertase 2, cluster of differentiation 44 (CD44), glutamic oxaloacetic transaminase 1 (GOT1), trefoil factor 3 (TFF3), Friedreich Ataxia gene (X123), fibroblast growth factor 13 (FGF13), carbonic anhydrase 4 (CA4), crystallin alpha-B (CRYAB), peptidylprolyl isomerase F (PPIF), asparagine synthase (ASNS), sodium channel, non-voltage gated, 1 alpha subunit (SCNN1A), frizzled homolog 1 (FZD1), tyrosine related protein 1 (TYRP1), E cadherin, type 1 (ECAD), and thyroid hormone receptor associated protein 220 (TRAP220). Of note, two of these biomarkers (GOT1 and CD44) are now used in the Afirma classifier assay. We chose to compare specifically FTC versus FA rather than include all histologic categories to create a more uniform immunohistochemical comparison. In addition, we have found that most papillary thyroid carcinoma could often be reasonably distinguished from benign disease by morphological cytology findings alone.

RESULTS

Increased immunoreactivity of CRYAB was associated with thyroid malignancy (c-statistic, 0.644; negative predictive value [NPV], 0.90) and loss of immunoreactivity of CA4 was also associated with malignancy (c-statistic, 0.715; NPV, 0.90) in indeterminate thyroid specimens. The combination of CA4 and CRYAB for discriminating FTC from FA resulted in a better c-statistic of 0.75, sensitivity of 0.76, specificity of 0.59, positive predictive value (PPV) of 0.32, and NPV of 0.91. When comparing widely angioinvasive FTC from FA, the resultant c-statistic improved to 0.84, sensitivity of 0.75, specificity of 0.76, PPV of 0.11, and NPV of 0.99.

CONCLUSIONS

Loss of CA4 and increase in CRYAB immunoreactivity distinguish FTC from FA in indeterminate thyroid nodules on a thyroid TMA with an NPV of 91%. Further studies in preoperative patient fine needle aspiration (FNAs) are needed to validate these results.

Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasound

Ultrasonography has great potential in differentiating malignant thyroid nodules from the benign ones. However, visual interpretation is limited by interobserver variability, and further, the speckle distribution poses a challenge during the classification process. This article thus presents an automated system for tumor classification in three-dimensional contrast-enhanced ultrasonography data sets. The system first processes the contrast-enhanced ultrasonography images using complex wavelet transform-based filter to mitigate the effect of speckle noise. The higher order spectra features are then extracted and used as input for training and testing a fuzzy classifier. In the off-line training system, higher order spectra features are extracted from a set of images known as the training images. These higher order spectra features along with the clinically assigned ground truth are used to train the classifier and obtain an estimate of the classifier or training parameters. The ground truth tells the class label of the image (i.e. whether the image belongs to a benign or malignant nodule). During the online testing phase, the estimated classifier parameters are applied on the higher order spectra features that are extracted from the testing images to predict their class labels. The predicted class labels are compared with their corresponding original ground truth to evaluate the performance of the classifier. Without utilizing the complex wavelet transform filter, the fuzzy classifier demonstrated an accuracy of 91.6%, while utilizing the complex wavelet transform filter, the accuracy significantly boosted to 99.1%.

Diagnosis of thyroid cancer: state of art

INTRODUCTION

Thyroid cancer is the most common endocrine cancer in the USA and its incidence is increasing worldwide. Thyroid fine-needle aspiration biopsy (FNA) and cytologic analysis is the most cost-effective approach to distinguish between malignant and benign thyroid nodules. However, up to 30% of thyroid FNA biopsy results are inconclusive.

AREAS COVERED

In this article, the authors provide an update on the current status and emerging approaches for improving thyroid cancer diagnosis. This review covers imaging, genetic and genomic approaches being used or in development to help distinguish between malignant and benign thyroid nodules.

EXPERT OPINION

There has been considerable progress in improving thyroid cancer diagnosis. The molecular markers analysis to avoid diagnostic surgeries seems to be promising. However, the clinical utility and accuracy of some markers reported in this review are not conclusive and need to be validated as clinical diagnostic tool.

Classification of follicular cell-derived thyroid cancer by global RNA profiling

The incidence of thyroid cancer is increasing worldwide and thyroid nodules are a frequent clinical finding. Diagnosing follicular cell-derived cancers is, however, challenging both histopathologically and especially cytopathologically. The advent of high-throughput molecular technologies has prompted many researchers to explore the transcriptome and, in recent years, also the miRNome in order to generate new molecular classifiers capable of classifying thyroid tumours more accurately than by conventional cytopathological and histopathological methods. This has led to a number of molecular classifiers that may differentiate malignant from benign thyroid nodules. Molecular classification models based on global RNA profiles from fine-needle aspirations are currently being evaluated; results are preliminary and lack validation in prospective clinical trials. There is no doubt that molecular classification will not only contribute to our biological insight but also improve clinical and pathological examinations, thus advancing thyroid tumour diagnosis and ultimately preventing superfluous surgery. This review evaluates the status of classification and biological insights gained from molecular profiling of follicular cell-derived thyroid cancers.

ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform

Using right equipment and well trained personnel, ultrasound of the neck can detect a large number of non-palpable thyroid nodules. However, this technique often suffers from subjective interpretations and poor accuracy in the differential diagnosis of malignant and benign thyroid lesions. Therefore, we developed an automated identification system based on knowledge representation techniques for characterizing the intra-nodular vascularization of thyroid lesions. Twenty nodules (10 benign and 10 malignant), taken from 3-D high resolution ultrasound (HRUS) images were used for this work. Malignancy was confirmed using fine needle aspiration biopsy and subsequent histological studies. A combination of discrete wavelet transformation (DWT) and texture algorithms were used to extract relevant features from the thyroid images. These features were fed to different configurations of AdaBoost classifier. The performance of these configurations was compared using receiver operating characteristic (ROC) curves. Our results show that the combination of texture features and DWT features presented an accuracy value higher than that reported in the literature. Among the different classifier setups, the perceptron based AdaBoost yielded very good result and the area under the ROC curve was 1 and classification accuracy, sensitivity and specificity were 100%. Finally, we have composed an Integrated Index called thyroid malignancy index (TMI) made up of these DWT and texture features, to facilitate distinguishing and diagnosing benign or malignant nodules using just one index or number. This index would help the clinicians in more quantitative assessment of the thyroid nodules.

Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan™ systems

Ultrasound-based thyroid nodule characterization into benign and malignant types is limited by subjective interpretations. This paper presents a Computer Aided Diagnostic (CAD) technique that would present more objective and accurate classification and further would offer the physician a valuable second opinion. In this paradigm, we first extracted the features that quantify the local changes in the texture characteristics of the ultrasound off-line training images from both benign and malignant nodules. These features include: Fractal Dimension (FD), Local Binary Pattern (LBP), Fourier Spectrum Descriptor (FS), and Laws Texture Energy (LTE). The resulting feature vectors were used to build seven different classifiers: Support Vector Machine (SVM), Decision Tree (DT), Sugeno Fuzzy, Gaussian Mixture Model (GMM), K-Nearest Neighbor (KNN), Radial Basis Probabilistic Neural Network (RBPNN), and Naive Bayes Classifier (NBC). Subsequently, the feature vector-classifier combination that results in the maximum classification accuracy was used to predict the class of a new on-line test thyroid ultrasound image. Two data sets with 3D Contrast-Enhanced Ultrasound (CEUS) and 3D High Resolution Ultrasound (HRUS) images of 20 nodules (10 benign and 10 malignant) were used. Fine needle aspiration biopsy and histology results were used to confirm malignancy. Our results show that a combination of texture features coupled with SVM or Fuzzy classifiers resulted in 100% accuracy for the HRUS dataset, while GMM classifier resulted in 98.1% accuracy for the CEUS dataset. Finally, for each dataset, we have proposed a novel integrated index called Thyroid Malignancy Index (TMI) using the combination of FD, LBP, LTE texture features, to diagnose benign or malignant nodules. This index can help clinicians to make a more objective differentiation of benign/malignant thyroid lesions. We have compared and benchmarked the system with existing methods.

Proteomic profiling of follicular and papillary thyroid tumors

OBJECTIVE

Thyroid proteomics is a new direction in thyroid cancer research aiming at etiological understanding and biomarker identification for improved diagnosis.

METHODS

Two-dimensional electrophoresis was applied to cytosolic protein extracts from frozen thyroid samples (ten follicular adenomas, nine follicular carcinomas, ten papillary carcinomas, and ten reference thyroids). Spots with differential expression were revealed by image and multivariate statistical analyses, and identified by mass spectrometry.

RESULTS

A set of 25 protein spots significant for discriminating between the sample groups was identified. Proteins identified for nine of these spots were studied further including 14-3-3 protein beta/alpha, epsilon, and zeta/delta, peroxiredoxin 6, selenium-binding protein 1, protein disulfide-isomerase precursor, annexin A5 (ANXA5), tubulin alpha-1B chain, and α1-antitrypsin precursor. This subset of protein spots carried the same predictive power in differentiating between follicular carcinoma and adenoma or between follicular and papillary carcinoma, as compared with the larger set of 25 spots. Protein expression in the sample groups was demonstrated by western blot analyses. For ANXA5 and the 14-3-3 proteins, expression in tumor cell cytoplasm was demonstrated by immunohistochemistry both in the sample groups and an independent series of papillary thyroid carcinomas.

CONCLUSION

The proteins identified confirm previous findings in thyroid proteomics, and suggest additional proteins as dysregulated in thyroid tumors.

The new molecular markers DDIT3, STT3A, ARG2 and FAM129A are not useful in diagnosing thyroid follicular tumors

Preoperative characterization of thyroid follicular lesions is challenging. Fine-needle aspiration specimens cannot differentiate follicular carcinomas from benign follicular neoplasias. Recently, promising markers have been detected using modern molecular techniques. We conducted a retrospective study to confirm the usefulness of immunohistochemical staining for the protein markers, DDIT3, STT3A (ITM1), ARG2 and FAM129A (C1orf24) in separating benign and malignant thyroid follicular lesions. Formalin-fixed, paraffin-embedded thyroid tissue from 30 in-house cases (15 follicular carcinomas and 15 follicular adenomas), as well as 8 follicular carcinomas and 21 follicular adenomas on tissue microarray slides were stained immunohistochemically for DDIT3, STT3A, ARG2 and FAM129A expression. Control tissue consisted of thyroid parenchyma adjacent to the tumors and 11 separate cases of normal thyroid parenchyma. All in-house cases of follicular adenomas, follicular carcinomas and adjacent normal thyroid tissue showed positive immunostaining with anti-DDIT3 and anti-STT3A. Anti-ARG2 and anti-FAM129A polyclonal antibodies showed positive staining in 20 and 60% of in-house follicular adenomas, and 40 and 87% of in-house follicular carcinomas, respectively. Monoclonal anti-FAM129A demonstrated positive staining in 13 and 33% of in-house follicular adenomas and follicular carcinomas, respectively. Polyclonal anti-DDIT3, -STT3A and -FAM129A antibodies showed positive staining in all tissue microarray slides of follicular carcinoma and in 76, 85 and 81% of the follicular adenomas, respectively. Monoclonal anti-STT3A stained 81% of the follicular adenoma cores. Anti-ARG2 stained positive in 13% of follicular carcinomas and 10% of follicular adenomas on the tissue microarray slides. In conclusion, DDIT3, STT3A, ARG2 and FAM129A immunohistochemistry does not appear to be useful in the diagnosis of thyroid follicular neoplasias, as they do not reliably distinguish follicular thyroid carcinoma from follicular thyroid adenoma.

A general method to derive robust organ-specific gene expression-based differentiation indices: application to thyroid cancer diagnostic

Differentiation is central to development, while dedifferentiation is central to cancer progression. Hence, a quantitative assessment of differentiation would be most useful. We propose an unbiased method to derive organ-specific differentiation indices from gene expression data and demonstrate its usefulness in thyroid cancer diagnosis. We derived a list of thyroid-specific genes by selecting automatically those genes that are expressed at higher level in the thyroid than in any other organ in a normal tissue's genome-wide gene expression compendium. The thyroid index of a tissue was defined as the median expression of these thyroid-specific genes in that tissue. As expected, the thyroid index was inversely correlated with meta-PCNA, a proliferation metagene, across a wide range of thyroid tumors. By contrast, the two indices were positively correlated in a time course of thyroid-stimulating hormone (TSH) activation of primary thyrocytes. Thus, the thyroid index captures biological information not integrated by proliferation rates. The differential diagnostic of follicular thyroid adenomas and follicular thyroid carcinoma is a notorious challenge for pathologists. The thyroid index discriminated them as accurately as did machine-learning classifiers trained on the genome-wide cancer data. Hence, although it was established exclusively from normal tissue data, the thyroid index integrates the relevant diagnostic information contained in tumoral transcriptomes. Similar results were obtained for the classification of the follicular vs classical variants of papillary thyroid cancers, that is, tumors dedifferentiating along a different route. The automated procedures demonstrated in the thyroid are applicable to other organs.

Cost-effective and non-invasive automated benign and malignant thyroid lesion classification in 3D contrast-enhanced ultrasound using combination of wavelets and textures: a class of ThyroScan™ algorithms

Ultrasound has great potential to aid in the differential diagnosis of malignant and benign thyroid lesions, but interpretative pitfalls exist and the accuracy is still poor. To overcome these difficulties, we developed and analyzed a range of knowledge representation techniques, which are a class of ThyroScan™ algorithms from Global Biomedical Technologies Inc., California, USA, for automatic classification of benign and malignant thyroid lesions. The analysis is based on data obtained from twenty nodules (ten benign and ten malignant) taken from 3D contrast-enhanced ultrasound images. Fine needle aspiration biopsy and histology confirmed malignancy. Discrete Wavelet Transform (DWT) and texture algorithms are used to extract relevant features from the thyroid images. The resulting feature vectors are fed to three different classifiers: K-Nearest Neighbor (K-NN), Probabilistic Neural Network (PNN), and Decision Tree (DeTr). The performance of these classifiers is compared using Receiver Operating Characteristic (ROC) curves. Our results show that combination of DWT and texture features coupled with K-NN resulted in good performance measures with the area of under the ROC curve of 0.987, a classification accuracy of 98.9%, a sensitivity of 98%, and a specificity of 99.8%. Finally, we have proposed a novel integrated index called Thyroid Malignancy Index (TMI), which is made up of texture features, to diagnose benign or malignant nodules using just one index. We hope that this TMI will help clinicians in a more objective detection of benign and malignant thyroid lesions.

Automated benign & malignant thyroid lesion characterization and classification in 3D contrast-enhanced ultrasound

In this work, we present a Computer Aided Diagnosis (CAD) based technique for automatic classification of benign and malignant thyroid lesions in 3D contrast-enhanced ultrasound images. The images were obtained from 20 patients. Fine needle aspiration biopsy and histology confirmed malignancy. Discrete Wavelet Transform (DWT) and texture based features were extracted from the thyroid images. The resulting feature vectors were used to train and test three different classifiers: K-Nearest Neighbor (K-NN), Probabilistic Neural Network (PNN), and Decision Tree (DeTr) using ten-fold cross validation technique. Our results show that combination of DWT and texture features in the K-NN classifier resulted in a classification accuracy of 98.9%, a sensitivity of 98%, and a specificity of 99.8%. Thus, the preliminary results of the proposed technique show that it could be adapted as an adjunct tool that can give valuable second opinions to the doctors regarding the nature of the thyroid nodule. The technique is cost-effective, non-invasive, fast, completely automated and gives more objective and reproducible results compared to manual analysis of the ultrasound images. We however intend to establish the clinical applicability of this technique by evaluating it with more data in the future.

Gene expression profile of human thyroid cancer in relation to its mutational status

This review describes the gene expression profile changes associated with the presence of different mutations that contribute to thyroid cell carcinogenesis. The results are discussed in the context of thyroid cancer biology and of the implications for disease prognosis, while the diagnostic aspect has been omitted. For papillary thyroid cancer (PTC), the most characteristic gene expression profile is associated with the presence of BRAF mutation. BRAF-associated PTC differ profoundly from RET/PTC or RAS-associated cancers. Simultaneously, they retain many characteristic gene expression features common for all PTCs, induced by the alternative mutations activating MAPK pathway. Although the difference between papillary and follicular thyroid cancer (FTC) is significant at the gene expression profile level, surprisingly, the RAS-related signature of FTC is not well specified. PAX8/peroxisome proliferator-activated receptor γ (PPARγ) rearrangements, which occur in FTC as an alternative to the RAS mutation, are associated with specific changes in gene expression. Furthermore, the difference between well-differentiated thyroid cancers and poorly differentiated and anaplastic thyroid cancers is mainly a reflection of tumor degree of differentiation and may not be attributed to the presence of characteristic mutations.

Galectin-3 and Beclin1/Atg6 genes in human cancers: using cDNA tissue panel, qRT-PCR, and logistic regression model to identify cancer cell biomarkers

Background

Cancer biomarkers are sought to support cancer diagnosis, predict cancer patient response to treatment and survival. Identifying reliable biomarkers for predicting cancer treatment response needs understanding of all aspects of cancer cell death and survival. Galectin-3 and Beclin1 are involved in two coordinated pathways of programmed cell death, apoptosis and autophagy and are linked to necroptosis/necrosis. The aim of the study was to quantify galectin-3 and Beclin1 mRNA in human cancer tissue cDNA panels and determine their utility as biomarkers of cancer cell survival.

Methods and Results

A panel of 96 cDNAs from eight (8) different normal and cancer tissue types were used for quantitative real-time polymerase chain reaction (qRT-PCR) using ABI7900HT. Miner2.0, a web-based 4- and 3- parameter logistic regression software was used to derive individual well polymerase chain reaction efficiencies (E) and cycle threshold (Ct) values. Miner software derived formula was used to calculate mRNA levels and then fold changes. The ratios of cancer to normal tissue levels of galectin-3 and Beclin1 were calculated (using the mean for each tissue type). Relative mRNA expressions for galectin-3 were higher than for Beclin1 in all tissue (normal and cancer) types. In cancer tissues, breast, kidney, thyroid and prostate had the highest galectin-3 mRNA levels compared to normal tissues. High levels of Beclin1 mRNA levels were in liver and prostate cancers when compared to normal tissues. Breast, kidney and thyroid cancers had high galectin-3 levels and low Beclin1 levels.

Conclusion

Galectin-3 expression patterns in normal and cancer tissues support its reported roles in human cancer. Beclin1 expression pattern supports its roles in cancer cell survival and in treatment response. qRT-PCR analysis method used may enable high throughput studies to generate molecular biomarker sets for diagnosis and predicting cancer treatment response.

STT3A, C1orf24, TFF3: putative markers for characterization of follicular thyroid neoplasms from fine-needle aspirates

OBJECTIVES/HYPOTHESIS

The goals of this study were to characterize gene expression using fine-needle aspirates (FNAs) from follicular neoplasms to distinguish follicular adenomas (FAs) from follicular thyroid carcinomas (FTCs) and follicular variant of papillary thyroid carcinomas (FVPTCs); and to use FNA material to distinguish benign from malignant follicular neoplasms.

STUDY DESIGN

Retrospective expression analysis of diagnosed follicular neoplasms (level of evidence 2b); prospective cohort of FNA from the operating room after thyroid lobectomy (level of evidence 1b).

METHODS

Gene expression analysis via reverse-transcription polymerase chain reaction (rt-PCR) of nine genes previously noted to be differentially expressed in follicular neoplasms was performed on formalin-fixed, paraffin-embedded archived normal thyroid tissue (n = 63) and follicular neoplasms as diagnosed on preoperative FNA: FA (n = 16), FTC (n = 13), FVPTC (n = 24), and papillary thyroid carcinomas (PTCs) (n = 10). All cases were originally read as follicular neoplasms on preoperative FNA. To determine if these results could be translated into fresh tissue, ex vivo FNA was performed on follicular neoplasms (n = 17) in the operating room after thyroidectomy.

RESULTS

Quantitative gene analysis detected differential TFF3 expression in FA versus FTC, FVPTC, and PTC (P = .02). Rt-PCR of FNA samples demonstrated that malignant nodules overexpress STT3A as compared with benign disease (P = .046). The combination of STT3A overexpression/Clorf24 underexpression identified malignant disease (P = .03) on FNA samples.

CONCLUSIONS

Gene-expression data suggest a difference in expression between STT3A, Clorf24, and TFF3 in FAs versus carcinomas that may be detected from an FNA sample. Findings must be validated from preoperative FNAs in larger numbers.

Validity of multiplex biomarker model of 6 genes for the differential diagnosis of thyroid nodules

Background

Currently the cytological examination of fine needle aspiration (FNA) biopsies is the standard technique for the pre-operative differential diagnosis of thyroid nodules. However, the results may be non-informative in ~20% of cases due to an inadequate sampling and the lack of highly specific, measurable cytological criteria, therefore ancillary biomarkers that could aid in these cases are clearly needed. The aim of our study was to evaluate the mRNA expression levels of 8 candidate marker genes as the diagnostic biomarkers for the discrimination of benign and malignant thyroid nodules and to find a combination of biomarkers with the highest diagnostic value.

Materials and methods

mRNA expression levels of eight candidate marker genes - BIRC5, CCND1, CDH1, CITED1, DPP4, LGALS3, MET and TFF3 was measured by real-time RT-PCR in paired nodular and surrounding normal thyroid tissue specimens of 105 consecutive patients undergoing thyroid surgery and compared between different types of thyroid lesions.

Results

Significant differences in the mRNA expression levels between the normal and malignant thyroid tissues and between benign and malignant nodules were found for BIRC5, CCND1, CITED1, DPP4, LGALS3, MET and TFF3, but not CDH1. On a single gene basis, relative quantity (RQ) of LGALS3 had the highest diagnostic value for the discrimination of malignant and benign thyroid nodules (AUC = 0.832, P < 0.0001 and 90.9% sensitivity and 65.6% specificity at the optimal cut-off on ROC curve). The only two-marker set that outperformed LGALS3 was RQ sum of LGALS3 and BIRC5 (AUC = 0.841, P < 0.0001). An application of multivariate logistic regression analysis resulted in the generation of a multiplex biomarker model based on LGALS3, BIRC5, TFF3, CCND1, MET and CITED1 that had considerably higher specificity than a single marker or two marker gene-based models (AUC = 0.895, P < 0.0001, 70.5% sensitivity and 93.4% specificity).

Conclusions

This study confirmed that mRNA expression levels of 7 out of 8 candidate genes analysed have a diagnostic value for the distinction of benign and malignant thyroid nodules. The multiplex biomarker model based on 6 genes outperformed a single marker or two marker-based models and warrants feasibility studies on FNA biopsies and the validation in a larger cohort of patients.

Differential expression of a set of genes in follicular and classic variants of papillary thyroid carcinoma

Fine-needle aspiration biopsy (FNA) is currently the best initial diagnostic test for evaluation of a thyroid nodule. FNA cytology cannot discriminate between benign and malignant thyroid nodules in up to 30% of thyroid nodules. Therefore, an adjunct to FNA is needed to clarify these lesions as benign or malignant. Using differential display-polymerase chain reaction method, the gene expression differences between follicular and classic variants of papillary thyroid carcinoma (PTC) and benign thyroid nodules were evaluated in a group of 42 patients. Computational gene function analyses via Cytoscape, FuncBASE, and GeneMANIA led us to a functional network of 17 genes in which a core sub-network of five genes coexists. Although the exact mechanisms underlying in thyroid cancer biogenesis are not currently known, our data suggest that the pattern of transformation from healthy cells to cancer cells of PTC is different in follicular variant than in classic variant.