Classification Of Thyroid Cancer Research Articles

Enhancing clinical decision-making: A novel nomogram for stratifying cancer-specific survival in middle-aged individuals with follicular thyroid carcinoma utilizing SEER data

BackgroundThyroid cancer (TC) is the most common malignant tumor in the endocrine system, is also one of the head and neck tumor. Follicular Thyroid Carcinoma (FTC) plays an important role in the pathological classification of thyroid cancer. This study aimed to develop an innovative predictive tool, a nomogram, for predicting cancer specific survival (CSS) in middle-aged FTC patients. MethodsWe collected patient data from the Surveillance, Epidemiology, and End Results (SEER) database. The data from patients between 2004 and 2015 were used as the training set, and the data from patients between 2016 and 2018 were used as the validation set. To identify independent risk factors affecting patient survival, univariate and multivariate Cox regression analyses were performed. Based on this, we developed a nomogram model aimed at predicting CSS in middle-aged patients with FTC. The consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration curve were used to evaluate the accuracy and confidence of the model. ResultsA total of 2470 patients were enrolled in this study, in which patients from 2004 to 2015 were randomly assigned to the training cohort (N = 1437) and validation cohort (N = 598), and patients from 2016 to 2018 were assigned to the external validation cohort (N = 435) in terms of time. Univariate and multivariate Cox regression analysis showed that marriage, histological grade and TNM stage were independent risk factors for survival. The C-index for the training cohort was 0.866 (95 % CI: 0.805–0.927), for the validation cohort it was 0.944 (95 % CI: 0.903–0.985), and for the external validation cohort, it reached 0.999 (95 % CI: 0.997–1.001). Calibration curves and AUC suggest that the model has good accuracy. ConclusionsWe developed an innovative nomogram to predict CSS in middle-aged patients with FTC. Our model after a rigorous internal validation and external validation process, based on the time proved that the high level of accuracy and reliability. This tool helps healthcare professionals and patients make informed clinical decisions.

Multi-omics analysis-based macrophage differentiation-associated papillary thyroid cancer patient classifier

BackgroundThe reclassification of Papillary Thyroid Carcinoma (PTC) is an area of research that warrants attention. The connection between thyroid cancer, inflammation, and immune responses necessitates considering the mechanisms of differential prognosis of thyroid tumors from an immunological perspective. Given the high adaptability of macrophages to environmental stimuli, focusing on the differentiation characteristics of macrophages might offer a novel approach to address the issues related to PTC subtyping. MethodsSingle-cell RNA sequencing data of medullary cells infiltrated by papillary thyroid carcinoma obtained from public databases was subjected to dimensionality reduction clustering analysis. The RunUMAP and FindAllMarkers functions were utilized to identify the gene expression matrix of different clusters. Cell differentiation trajectory analysis was conducted using the Monocle R package. A complex regulatory network for the classification of Immune status and Macrophage differentiation-associated Papillary Thyroid Cancer Classification (IMPTCC) was constructed through quantitative multi-omics analysis. Immunohistochemistry (IHC) staining was utilized for pathological histology validation. ResultsThrough the integration of single-cell RNA and bulk sequencing data combined with multi-omics analysis, we identified crucial transcription factors, immune cells/immune functions, and signaling pathways. Based on this, regulatory networks for three IMPTCC clusters were established. ConclusionBased on the co-expression network analysis results, we identified three subtypes of IMPTCC: Immune-Suppressive Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (ISMPTCC), Immune-Neutral Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (INMPTCC), and Immune-Activated Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (IAMPTCC). Each subtype exhibits distinct metabolic, immune, and regulatory characteristics corresponding to different states of macrophage differentiation.

Critical appraisal of the WHO 2022 classification of thyroid cancer

The WHO Classification of Endocrine and Neuroendocrine Tumors, Fifth Edition, is a current publication that includes relevant revisions to the nomenclature, grading, and prognostication of endocrine tumors based on pathologic characteristics and molecular profile. Additional entities such as thyroid follicular nodular disease, follicular adenoma with papillary architecture, and oncocytic adenoma of the thyroid are included in the benign follicular cell-derived category. Fifth edition WHO has refined the spectrum of papillary thyroid carcinoma (PTC) by aligning invasive encapsulated follicular variant PTCs with follicular thyroid carcinomas. Follicular cell-derived differentiated thyroid carcinomas may also have high-grade features in the absence of morphologic dedifferentiation, and hence, differentiated thyroid carcinomas with high-grade features are now recognized as a distinct morphologic spectrum. The Ki-67 proliferation index reporting is required in all medullary thyroid carcinomas for grading.

Gorilla Troops Optimizer with Deep Learning-Based Thyroid Cancer Classification on Histopathological Images

Gorilla Troops Optimizer with Deep Learning-Based Thyroid Cancer Classification on Histopathological Images

Integrated proteomic and metabolomic analysis of plasma reveals regulatory pathways and key elements in thyroid cancer.

Thyroid cancer (TC) is the most common endocrine malignancy with increasing incidence in recent years. Fine-needle aspiration biopsy (FNAB), as a gold standard for the initial evaluation of thyroid nodules, fails to cover all the cytopathologic conditions resulting in overdiagnosis. There is an urgent need for a better classification of thyroid cancer from benign thyroid nodules (BTNs). Here, data independent acquisition (DIA)-based proteomics and untargeted metabolomics in plasma samples of 10 patients with TC and 15 patients with BTNs were performed. Key proteins and metabolites were identified specific to TC, and an independent cohort was used to validate the potential biomarkers using enzyme-linked immunosorbent assay (ELISA). In total, 1429 proteins and 1172 metabolites were identified. Principal component analysis showed a strong overlap at the proteomic level and a significant discrimination at the metabolomic level between the two groups, indicating a more drastic disturbance in the metabolome of thyroid cancer. Integrated analysis of proteomics and metabolomics shows glycerophospholipid metabolism and arachidonic acid metabolism as key regulatory pathways. Furthermore, a multi-omics biomarker panel was developed consisting of LCAT, GPX3 and leukotriene B4. Based on the AUC value for the discovery set, the classification performance was 0.960. The AUC value of the external validation set was 0.930. Altogether, our results will contribute to the clinical application of potential biomarkers in the diagnosis of thyroid cancer.

Improving automated thyroid cancer classification of frozen sections by the aid of virtual image translation and stain normalization

Frozen sections are rapidly generated during surgical interventions. This allows surgeons to wait for histological findings during the interventions in order to base intra-surgical decisions on the outcome of the histology. However, compared to paraffin sections the quality of frozen sections is often strongly reduced, leading to a lower diagnostic accuracy. Deep learning-based image translation technology facilitates a virtual conversion between different native imaging technologies with the potential of translating a frozen section into a virtual paraffin section. Stain normalization can be applied to adjust further unequal image characteristics. We investigated the effect of deep learning-based image translation, conventional image normalization and a combination of these techniques on computer aided decision support systems for thyroid cancer diagnostics. For classification, a bag-of-words approach, based on convolutional neural network features, k-means clustering and a support vector machine were employed. While stain normalization led to a decreased overall classification accuracy (0.703 vs 0.727), image translation led to an increased mean score (0.770). A combination of both, image translation and normalization increased the accuracy even further (0.844) and clearly reduced the gap to the post-operative paraffin sections (0.902). Deep learning-based image translation proved to be a powerful tool to enhance accuracy of computer aided diagnosis which clearly outperformed conventional stain translation. This work provides a strong motivation for performing a study with expert pathologists performing the categorization of frozen sections and the corresponding improved sections, to investigate whether a similar effect is achieved in a clinical setting.

Deep Learning based Classification of Thyroid Cancer using Different Medical Imaging Modalities : A Systematic Review

Deep Learning based Classification of Thyroid Cancer using Different Medical Imaging Modalities : A Systematic Review

Added Value of Superb Microvascular Imaging and Virtual Touch Imaging Quantification in Assisting Thyroid Cancer Classification

This prospective study determined the value added by superb microvascular imaging (SMI) and Virtual Touch imaging quantification (VTIQ) to conventional ultrasonography in classifying malignant and benign thyroid nodules. One hundred eighty-three thyroid nodules (TNs) in 120 patients (112 benign and 71 malignant TNs) were evaluated. SMI revealed noticeable variance between benign and malignant TNs (p < 0.001). Malignant nodules tended to have rich vascularity (grade 3: 38/71, 53.5%) compared with benign nodules (grade 3: 33/112, 29.5%). There is a statistically significant difference between malignant and benign TNs with respect to shear-wave speed (SWS) values (all p values <0.001). The SWS mean, maximum and ratio of malignant nodules were 3.97 ± 1.34, 4.79 ± 1.70 and 1.25 ± 0.39, respectively. The SWS mean, maximum and ratio of benign nodules were 2.65 ± 0.42, 2.97 ± 0.46 and 1.15 ± 0.35, respectively. With respect to area under the curve values, the combined use of SMI or VTIQ improved the diagnostic performance of classifying malignant and benign TNs compared with that of ultrasonography alone. The combination of three modalities achieved the greatest area under the curve values (0.9811, 95% confidence interval: 0.95529–1.000), followed by US + VTIQ (0.9747, 0.94543–1.000), US + SMI (0.9032, 0.85345–0.95391) and ultrasonography (0.8291, 0.76417–0.89403).

Diagnostic Potential of Plasma IgG N-glycans in Discriminating Thyroid Cancer from Benign Thyroid Nodules and Healthy Controls.

BackgroundNovel biomarkers are urgently needed to distinguish between benign and malignant thyroid nodules and detect thyroid cancer in the early stage. The associations between serum IgG N-glycosylation and thyroid cancer risk have been revealed. We aimed to explore the potential of IgG N-glycan traits as biomarkers in the differential diagnosis of thyroid cancer.MethodsPlasma IgG N-glycome analysis was applied to a discovery cohort followed by independent validation. IgG N-glycan profiles were obtained using a robust quantitative strategy based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. IgG N-glycans were relatively quantified, and classification performance was evaluated based on directly detected and derived glycan traits.ResultsFour directly detected glycans were significantly changed in thyroid cancer patients compared to that in non-cancer controls. Derived glycan traits and a classification glycol-panel were generated based on the directly detected glycan traits. In the discovery cohort, derived trait BN (bisecting type neutral N-glycans) and the glyco-panel showed potential in distinguishing between thyroid cancer and non-cancer controls with AUCs of 0.920 and 0.917, respectively. The diagnostic potential was further validated. Derived trait BN and the glycol-panel displayed “accurate” performance (AUC>0.8) in discriminating thyroid cancer from benign thyroid nodules and healthy controls in the validation cohort. Meanwhile, derived trait BN and the glycol-panel also showed diagnostic potential in detecting early-stage thyroid cancer.ConclusionsIgG N-glycome analysis revealed N-glycomic differences that allow classification of thyroid cancer from non-cancer controls. Our results suggested that derived trait BN and the classification glyco-panel rather than single N-glycans may serve as candidate biomarkers for further validation.

Association between Body Mass Index and Severity According of Classification of Thyroid Cancer

Association between Body Mass Index and Severity According of Classification of Thyroid Cancer

Association between Body Mass Index and Severity According of Classification of Thyroid Cancer

Association between Body Mass Index and Severity According of Classification of Thyroid Cancer

Anton Wolfler - forgotten genius of surgery

Nowadays, when surgery as a science was so rapidly developed, the names of many scientists who stood at its origins are forgotten or rarely mentioned, and their contribution remains underestimated. One of them is the innovator of surgery of the XIX century. Czech scientist Anton Welfler, whose bold ideas have spread far beyond the boundaries of abdominal surgery. Unfortunately, for many he is known only as the author of the methodology for the formation of gastroenteroanastomosis and as a student of Theodore Billroth. However, in reality, A. Welfler made an incomparably greater contribution not only to gastric surgery, but also to herniology, thyroid surgery. The result of his study of embryogenesis, the histological structure of the thyroid gland, his own clinical experience gained two monographs, subsequently recognized as fundamental in this section of surgery. He developed a histological classification of thyroid cancer, for the first time described both solid and cystic forms of papillary cancer. He also suggested the possible mechanisms of carcinogenesis. Together with Theodore Billroth A. Welfler introduced the technique of total thyroidectomy into clinical practice. Widely used in modern surgical practice, gastroenteroanastomosis with the loop of the small intestine turned off and the U-shaped inter-intestinal anastomosis, named after Caesar Roux, was in fact, developed by A. Welfler in the experiment. Caesar Roux himself noted A. Welfler’s priority in the invention of this design. In 1897, Caesar Roux modified the method of A. Welfler, forming a posterior-free version of this operation.

Decision Support System for Classification Medullary Thyroid Cancer

Due to the complex, heterogeneous and mimic morphological features of medullary thyroid cancer (MTC). It becomes often difficult to diagnose MTC at early stage. Since histopathological complex patterns of cancerous cells and tissues requires a huge effort to classify. Therefore thyroid cancer classification has become one of the significant research area area(s) of Machine Learning. We propose a decision support system to classify initial variation of morphological appearance of nuclei by using Convolutional Neural Networks (CNNs). The system comprises over three major layers, where image preprocessing techniques are used at top layer along with feature selection techniques. Classification model constructed by using CNNs at the second layer and result visualization described at third layer. Due to the unavailability of datasets for medullary thyroid cancer in literature, this research uses real-world datasets consisting upon 20GB cytological medical images and the approximated classification accuracy is measured about 99.00%. Malignant and non - malignant cells are visualized to assist the doctors in better way.

Metabolite analysis-aided diagnosis of papillary thyroid cancer.

Thyroid cancer is the most frequent endocrine tumor with a growing incidence worldwide. However, common diagnostic strategy for thyroid cancer classification is hardly to make a proper diagnosis in some cases. To assist classical approach, this study used metabolomics to screen and validate biomarkers from serum and urinary for papillary thyroid cancer (PTC). Overall, 124 untreated PTC, 76 untreated benign thyroid nodule (BTN), and 116 healthy control (HC) were collected in this study. Thirty-six differential metabolites were screened from non-targeted metabolomics with a discovery sample set in comparison with HC and BTN. Serum β-hydroxybutyrate (BHB), docosahexaenoic acid (DHA), 1-methyladenosine (1-MedA), pregnanediol-3-glucuronide (PdG), urinary nicotinic acid mononucleotide (NAM) and xanthosine (Xan) were validated to be significantly differential by targeted metabolomics in validation set. The logistic regression model incorporating six biomarkers had excellent discrimination from receiver-operating characteristics (ROC) analysis, with area under the receiver-operating characteristic curve (AUC) of 0.943 (95% CI 0.902 to 0.983) and 0.952 (95% CI 0.921 to 0.983) for female alone and female + male samples, respectively. The prediction accuracy and false-negative rate in the real setting of one PTC to ten suspicious nodules were 84.7 and 17.7% with the threshold at probablity of 0.5. Results of a double-blind study for PTC and BTN had true positive value of 100% and true negative value of 91.7%. To conclude, BHB, DHA, 1-MedA, PdG, NAM and Xan are suitable biomarkers for PTC, and logistic regression models with the six biomarkers can be potentially used as clinical diagnosis.

Oncometabolites as biomarkers in thyroid cancer: a systematic review.

IntroductionThyroid cancer (TC) is an important common endocrine malignancy, and its incidence has increased in the past decades. The current TC diagnosis and classification tools are fine-needle aspiration (FNA) and histological examination following thyroidectomy. The metabolite profile alterations of thyroid cells (oncometabolites) can be considered for current TC diagnosis and management protocols.MethodsThis systematic review focuses on metabolite alterations within the plasma, FNA specimens, and tissue of malignant TC contrary to benign, goiter, or healthy TC samples. A systematic search of MEDLINE (PubMed), Scopus, Embase, and Web of Science databases was conducted, and the final 31 studies investigating metabolite biomarkers of TC were included.ResultsA total of 15 targeted studies and 16 untargeted studies revealed several potential metabolite signatures of TC such as glucose, fructose, galactose, mannose, 2-keto-d-gluconic acid and rhamnose, malonic acid and inosine, cholesterol and arachidonic acid, glycosylation (immunoglobulin G [IgG] Fc-glycosylation), outer mitochondrial membrane 20 (TOMM20), monocarboxylate transporter 4 (MCT4), choline, choline derivatives, myo-/scyllo-inositol, lactate, fatty acids, several amino acids, cell membrane phospholipids, estrogen metabolites such as 16 alpha-OH E1/2-OH E1 and catechol estrogens (2-OH E1), and purine and pyrimidine metabolites, which were suggested as the TC oncometabolite.ConclusionCitrate was suggested as the first most significant biomarker and lactate as the second one. Further research is needed to confirm these biomarkers as the TC diagnostic oncometabolite.

A Survey on Diagnosis of US Image Thyroid Nodules and Automated Classification

Different types of human diseases are detected by medical image analysis which plays an important role. Studies that are developed for automated thyroid cancer classification is reviewed in this paper, especially to analyze the benign and malignant thyroid nodules features and comparisons. Hypothyroidism, hyperthyroidism, goitre and thyroid nodules (benign/malignant) are thyroid disorders. Ultrasound imaging, CT, MR imaging, nuclear medicine (NM) with positron emission tomography (PET), single photon emission computed tomography (SPECT) are the different medical techniques used to identify and classify thyroid gland abnormalities. In order to enhance the diagnosis of thyroid disease, various image processing techniques applied to thyroid ultra sound images are reviewed here. Studies based on non-clinical features for thyroid nodules classification is also discussed and reviewed.

Thrombosis in Thyroid Cancer.

BackgroundThe number of studies on venous thromboembolism (VTE) and thyroid cancer is very scarce and existing data are contradictory. This paper reviews VTE in thyroid cancer.MethodsThe following words were used for a comprehensive literature review using MEDLINE database: Blood coagulation factors; thyroid hormones; blood coagulation tests; venous thromboembolism; receptors thyroid hormone; hemostasis; fibrinolysis; bleeding; blood coagulation disorders; thyroid neoplasms; Thyroid cancer, papillary; Thyroid cancer, follicular; Thyroid carcinoma, anaplastic; Thyroid cancer, Hurthle cell; Familial medullary thyroid carcinoma; venous thrombosis; Pulmonary embolism; Blood coagulation factors. The studies, which include any changes in hemostasis and thyroid cancer were included and reviewed.ResultsAlthough few studies have shown a possible increase in VTE occurrence in thyroid cancer in patients ≥ 60 years old and in proximity to cancer diagnosis, other studies could not find any difference compared to general population. New thyroid cancer classification excluding common subtype(s) with benign nature, may affect the results of the future studies on association of VTE and thyroid cancer.ConclusionsProspective studies on the occurrence of VTE in various types and severities of thyroid cancer and in different age groups are warranted, as the results would affect clinical practice on the necessity of usage of anticoagulants in some thyroid cancer groups.

Metabolic Alterations of Thyroid Cancer as Potential Therapeutic Targets.

Thyroid cancer (TC) is the most frequent endocrine tumor with a growing incidence worldwide. Besides the improvement of diagnosis, TC increasing incidence is probably due to environmental factors and lifestyle modifications. The actual diagnostic criteria for TC classification are based on fine needle biopsy (FNAB) and histological examination following thyroidectomy. Since in some cases it is not possible to make a proper diagnosis, classical approach needs to be supported by additional biomarkers. Recently, new emphasis has been given to the altered cellular metabolism of proliferating cancer cells which require high amount of glucose for energy production and macromolecules biosynthesis. Also TC displays alteration of energy metabolism orchestrated by oncogenes activation and tumor suppressors inactivation leading to abnormal proliferation. Furthermore, TC shows significant metabolic heterogeneity within the tumor microenvironment and metabolic coupling between cancer and stromal cells. In this review we focus on the current knowledge of metabolic alterations of TC and speculate that targeting TC metabolism may improve current therapeutic protocols for poorly differentiated TC. Future studies will further deepen the actual understandings of the metabolic phenotype of TC cells and will give the chance to provide novel prognostic biomarkers and therapeutic targets in tumors with a more aggressive behavior.

Follicular cell thyroid neoplasia: insights from genomics and The Cancer Genome Atlas research network.

The present review is focused on the recently published study on the genomics of papillary thyroid carcinoma performed by The Cancer Genome Atlas Research Network and its implications for the follicular variant of papillary carcinoma. The Cancer Genome Atlas study of papillary thyroid carcinoma comprehensively examined the cancer genome of nearly 500 primary tumors. Using a highly integrated bioinformatic analysis, papillary carcinoma was shown at the genomic level to consist of two highly distinct classes that reflected both tumor histology and underlying genotype. Tumors with true papillary architecture were dominated by BRAF(V600E) mutations and RET kinase fusions and were designated as BRAF(V600E)-like. Tumors with follicular architecture were conversely dominated by RAS mutations and were designated as RAS-like. Given the strong genotype:phenotype correlation known to be present in thyroid cancer, the separation of BRAF(V600E)-like and RAS-like tumors has profound implications for its classification, especially the follicular variant of papillary carcinoma. The recent genomic characterization of papillary thyroid carcinoma is challenging the established pathological classification of thyroid cancer with significance for the care of patients.

Application of metabolomics in thyroid cancer research.

Thyroid cancer is the most common endocrine malignancy with four major types distinguished on the basis of histopathological features: papillary, follicular, medullary, and anaplastic. Classification of thyroid cancer is the primary step in the assessment of prognosis and selection of the treatment. However, in some cases, cytological and histological patterns are inconclusive; hence, classification based on histopathology could be supported by molecular biomarkers, including markers identified with the use of high-throughput “omics” techniques. Beside genomics, transcriptomics, and proteomics, metabolomic approach emerges as the most downstream attitude reflecting phenotypic changes and alterations in pathophysiological states of biological systems. Metabolomics using mass spectrometry and magnetic resonance spectroscopy techniques allows qualitative and quantitative profiling of small molecules present in biological systems. This approach can be applied to reveal metabolic differences between different types of thyroid cancer and to identify new potential candidates for molecular biomarkers. In this review, we consider current results concerning application of metabolomics in the field of thyroid cancer research. Recent studies show that metabolomics can provide significant information about the discrimination between different types of thyroid lesions. In the near future, one could expect a further progress in thyroid cancer metabolomics leading to development of molecular markers and improvement of the tumor types classification and diagnosis.