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Thyroid Cytology

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Micrograph showing the cytology of papillary thyroid carcinoma in quick hematoxylin-eosin stain.

Thyroid cytology is the microscopic study of cells in the thyroid gland. It is a subfield of cytopathology that plays an important role in the diagnosis and research of a variety of thyroid abnormalities.[1]

In clinical diagnosis, thyroid cytology samples are commonly obtained from patient thyroid nodules or cysts using fine needle aspiration (FNA) techniques.[2] These samples consist of cellular components, including follicular cells, hurthle cells, parafollicular cells, and immune cells; they also contain acellular components such as the colloid.[1] These components are further visualized with various staining techniques. Stained samples can be assessed under The Bethesda System for Reporting Thyroid Cytology (TBSRTC) guidelines to evaluate the risks of malignancy.[2]

In recent decades, thyroid cytology has also incorporated clinical ancillary services regarding molecular testing for functional biomarkers and emerging technologies for prognosis to support clinical treatment and therapeutic decision-making.[2]

Sample preparation techniques

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Sampling Methods

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Fine needle aspiration cytology

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Fine needle aspiration (FNA) cytology is the major sampling method used in thyroid cytology.[3] It is a highly sensitive and cost-effective method that allows reliable evaluation of patient thyroid nodules. Therefore, patients with malignant nodules can be provided with appropriate surgical intervention. Meanwhile, fine needle aspiration can also prevent unnecessary treatment for patients with benign tumors, as the majority of thyroid nodules are benign rather than malignant. However, fine needle aspiration contains certain technical drawbacks. One common issue is the negative pressure applied during aspiration. It might accidentally draw blood from the patients into samples. Blood smears on the slides can alter the cell concentration and morphology of the sample, which leads to an unsatisfactory specimen.[4]

Malignant thyroid nodules in Papanicolaou stain.

Fine needle nonaspiration cytology

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Enlarged thyroid follicular cells in hematoxylin-eosin stain.

Compared to fine needle aspiration cytology, fine needle nonaspiration cytology does not require active aspiration. It reduces bleeding and thyroid tissue trauma for patients during sampling. However, the diagnostic adequacy might be lower than fine needle aspiration cytology.[4]

Staining methods

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Staining is an essential technique used to visualize cellular structure and components on a microscopic level. One of the most widely used staining methods in thyroid cytology is Papanicolaou stain, while Hematoxylin-eosin stain, Romanowsky staining and immunocytochemistry are also commonly used staining methods. Different staining methods allow pathologists to optimize observations for different cellular populations and characteristics. However, the background factors and the measuring parameters did not significantly differ among different staining methods.[5]

Cytological components

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Cellular Components

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Follicular cells

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Micrograph showing the cytology of benign thyroid follicular cell pattern with abundant colloid in Papanicolaou stain.

Follicular cells are the main cellular population of the thyroid. They secrete triiodothyronine (T3) and tetraiodothyronine (T4), collectively known as thyroid hormones, which are essential for human growth and development.[6]

Normal follicular cells have a flat, regular shape and are characterized by oval and uniformly colored nuclei with a moderate amount of cytoplasm.[7] In neoplasms, the follicular cell structure and nuclear features deviate from their normal counterpart, featuring enlarged oval nuclei, light-colored chromatin, and longitudinal nuclear grooves. These appearances imply follicular thyroid cancer.[7]

The cytoarchitecture of follicular cells also helps to differentiate between benign and malignant thyroid nodules. Benign nodules contain a macrofollicular pattern, which is characterized by flat sheets of follicular cells and accompanied by abundant colloid.[8] Malignant thyroid nodules, on the other hand, usually display a microfollicular pattern, which is characterized by flat sheets of follicular cells with dark and round nuclei arranged in a compact, honeycomb-like structure. This microfollicular pattern is often associated with follicular neoplasms and carcinomas.[8]

Hurthle cells

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Micrograph showing Hurthle cells derived from thyroid fine needle aspiration specimen in hematoxylin-eosin stain.

Hurthle cell is a type of thyroid oncocyte. They are a variant of thyroid follicular cells and are not typically observed in normal thyroid samples.[8]

Hurthle cells contain an abundant amount of fine, granular cytoplasm with enlarged and prominently-colored nuclei.[9] Their nuclear size is highly variable across populations, and all individuals have clearly defined cell borders.[8][9] Hurthle cells are present in a wide spectrum of thyroid lesions, including Hashimoto’s thyroiditis, Hürthle cell neoplasm, and sometimes in benign nodular hyperplasia.[9]

Parafollicular (C) cells

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Parafollicular cells, also known as clear cells, produce a hormone known as calcitonin to assist in homeostasis and bone formation.[10] They are characterized by a small to medium cell size with eccentric nuclei and granular cytoplasm.[11] Parafollicular cells are seldom observed in normal cytological samples, and their presence would implicate medullary thyroid carcinoma.[11][12]

Lymphocytes

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Lymphocytes are a collective population of white blood cells, including T cells, B cells, and NK cells that make up the body’s immune system. They are characterized by large nuclei with a high nucleus-to-cytoplasm ratio. In thyroid cytology, samples containing a significant accumulation of lymphocytes associated with follicular or oncocyte populations often implicate Hashimoto’s thyroiditis or Graves disease.[8]

Macrophages

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Macrophages make up a part of the body’s innate immune system. They are a type of white blood cells characterized by their vacuolated or “foamy” cytoplasm and irregularly shaped nuclei.[1] In thyroid cytology, macrophages are regularly found in patient samples of thyroid cysts. These cells may also contain pigmentated materials such as hemosiderin, which gives their cytoplasm a powdery appearance.[13]. However, their appearance alone does not constitute a parameter for diagnosis or warrant additional clinical concerns.[13]

Acellular Components

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Colloid

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Micrograph showing follicular cells and colloid derived from thyroid fine needle aspiration specimen in Papanicolaou stain.

Colloid is a gelatinous, viscous substance stored in the thyroid gland, enveloped by a simple layer of thyroid follicular cells. It is mainly composed of thyroglobulin and serves as the raw materials for thyroid hormone synthesis.[14] The accumulation of colloid would form colloid nodules, which is a type of frequently examined thyroid nodule. In thyroid cytology, an abundant, thick colloid usually suggests a benign colloid nodule. Whereas a limited amount of colloid content, especially when observed alongside oncocytes, would raise concerns for neoplastic lesions.[14]

Background vascular and stromal components

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In thyroid cytology samples, background fragments such as vascular, stromal, neural, or skeletal muscle tissue may be observed. Although these components do not directly indicate a particular type of thyroid abnormalities, their appearance may still implicate an underlying pathological mechanism.[1]

Diagnostic categories

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Bethesda System for Reporting Thyroid Cytology

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In thyroid cytology, the Bethesda System for Reporting Thyroid Cytology is commonly applied to evaluate fine needle aspiration specimens. It allows the generalization of diagnostic categories, thereby facilitating communication between medical workers as well as research professionals.[3]

The categorization is stated below:

I. Nondiagnostic (5-20% cancer risk)

Features of the nodules might be unclear or unidentified. The thyroid sample might also be unsatisfactory, with inadequate cell tissue for diagnosis. For a thyroid fine needle aspiration sample to be satisfactory for evaluation, the sample should include at least 6 groups of follicular cells, with each group containing at least 10 cells.[15]

II. Benign (2-7% cancer risk)

Benign tumors are noncancerous tumors.

III. Atypia of Undetermined Significance (13-30% cancer risk)

Thyroid cells under this category have structural abnormalities but it is difficult to classify into the benign, suspicious, or malignant categories.[16]

IV. Follicular Neoplasm (23-34% cancer risk)

It refers to abnormal growth in thyroid follicular cells which might develop into follicular thyroid cancer.[15]

V. Suspicious for Malignancy (67-83% cancer risk)

Only a few characteristic features of a cancer cell can be diagnosed in this category. The malignant cells might not be widespread, therefore a certain malignant diagnosis cannot be made.[15]

VI. Malignant (97-100% cancer risk)

Thyroid cells under this category exhibit conclusive features for malignancy.[16]

Image of Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), which is malignant.

Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP)

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There are noninvasive tumors that demonstrate subtle abnormalities in cell nucleus called Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). NIFTP can be classified as benign, but its indolent nature might lead to malignancy with very low potential. However, it might cause overtreatment if NIFTPs are defined as malignant. Therefore, in the third edition of the Bethesda System for reporting thyroid cytology, the authors included two versions of risks of malignancy of the case: including and excluding NIFTP as malignant.[16][17][18]

Clinical utility and future directions

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Integration with radiological findings

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Ultrasound image of thyroid nodules.

Ultrasound imaging can be coupled with fine needle aspiration. It assesses the sonographic features of thyroid nodules in a non-invasive manner to predict the risk of malignancy of the nodules. These features allow screening for benign nodules, thereby diminishing healthcare costs and reducing unwarranted fine needle aspiration procedures.[19] The results of ultrasound imaging are reported in Thyroid Imaging, Reporting and Data System (TIRADS).[20] In addition to TIRADS, the British Thyroid Association also provided an alternative version of guidelines to classify ultrasound imaging results.

Ancillary Services

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Various molecular tests are widely used after thyroid cytology for ancillary service. Thus, the exact risk of malignancy in cases that cannot be certainly identified as benign or malignant can be determined. Hence, suitable management options can be carried out. Common commercially available ancillary molecular tests include the Afirma GEC test, RosettaGX Reveal test, ThyroSeq and ThyGenX/ThyraMIR.[17]

Afirma GEC and RosettaGX Reveal tests

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Afirma GEC and RosettaGX Reveal tests evaluate expression profiles of selected messenger RNAs or microRNAs. Thus, genes and microRNA patterns expressed by malignant thyroid tumors can be recognized in the sample. The tests are highly sensitive to thyroid cancer, allowing identification of nodules with very low risks of malignancy.

Next Generation Sequencing

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A slide used for next generation sequencing.

Next Generation Sequencing (NGS) is widely applied to investigate the genetic causes behind the abnormal growth of thyroid cells in patients. Abnormal growth of thyroid cells might be correlated with gene mutation. To detect these genetic mutations, different genomic classifiers, such as ThyGeNEXT/ThyraMIR v2, Veracyte Afirma, and ThyroSeq v3 are marketed to detect mutation in the genome. Using next generation sequencing techniques, these platforms also identify the relationship between genomic disorders and abnormal growth of thyroid cells.[21]

Some next generation sequencing tools also combined sequencing with profiling approaches. For example, ThyGenX/ThyraMIR combined expression profiling and sequencing, which allows comprehensive analysis of thyroid nodules.[17] It also encapsulates the use of an algorithm for precise diagnosis of the risk of malignancy of the sample.

Single-gene tests

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Single-gene tests help detect the existence of mutated cancer-causing genes in the patient thyroid sample that indicate specific subtypes of thyroid cancer.[22] For instances, gene mutations in BRAFV600E, HRAS, NRAS, KRAS are more likely to be associated with thyroid neoplasms.[17] Therefore single-gene tests of these genes are often performed after thyroid cytology.

Emerging techniques

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Prognostication systems

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Prognostication systems can predict the clinical risk of disease development and determine better treatment options. It can also deduce presurgical molecular risk stratification information. American Joint Committee on Cancer (AJCC) and American Thyroid Association have published the staging criteria for stratification of patient prognostication.[21]

Artificial intelligence

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Artificial intelligence systems may assist in the analysis of thyroid samples. It can be applied to refine the evaluation of thyroid fine needle aspiration results by using different algorithms. These algorithms are trained on images of various thyroid nodules in different categories, clinical data, and ultrasound characteristics. Therefore, the artificial intelligence system can interpret complex thyroid fine needle aspiration results. They also shorten the time required for scanning cytology specimens.[23]

Specifically, convolutional neural networks can be utilized for analysis. It is one of the deep learning-based methods that can discover patterns directly from raw data under training with various images of thyroid nodules. It can retain spatial information during the transition between different layers in the sample, facilitating the diagnosis.[24]

References

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  1. ^ a b c d Das, Sambit; Agarwal, Vishal; Thakuria, Sailendra Kumar (January 2024). "Interpreting thyroid fine-needle aspiration biopsy". Thyroid Research and Practice. 20 (1): 15–21. doi:10.4103/trp.trp_23_23.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  2. ^ a b c Jin, Xiaobing; Jing, Xin (June 2024). "Cytologic assessment of thyroid nodules – Updates in 2023 Bethesda reporting system, diagnostic challenges and pitfalls". Human Pathology Reports. 36: 300743. doi:10.1016/j.hpr.2024.300743.
  3. ^ a b Tuluc, Madalina; Solomides, Charalambos (August 2014). "Thyroid Cytology". Otolaryngologic Clinics of North America. 47 (4): 475–489. doi:10.1016/j.otc.2014.04.011.
  4. ^ a b Song, Hongming; Wei, Chuankui; Li, Dengfeng; Hua, Kaiyao; Song, Jialu; Maskey, Niraj; Fang, Lin (September 2015). "Comparison of Fine Needle Aspiration and Fine Needle Nonaspiration Cytology of Thyroid Nodules: A Meta-Analysis". BioMed Research International. 2015: 1–13. doi:10.1155/2015/796120. ISSN 2314-6133. PMC 4603312. PMID 26491689.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  5. ^ Liu, Chih-Yi; Chen, Chien-Chin; Bychkov, Andrey; Agarwal, Shipra; Zhu, Yun; Hang, Jen-Fan; Lai, Chiung-Ru; Na, Hee Young; Park, So Yeon; Li, Weiwei; Liu, Zhiyan; Jain, Deepali; Suzuki, Ayana; Hirokawa, Mitsuyoshi; Chia, Noel (August 2021). "Constitutive Cytomorphologic Features of Medullary Thyroid Carcinoma Using Different Staining Methods". Diagnostics. 11 (8): 1396. doi:10.3390/diagnostics11081396. ISSN 2075-4418. PMC 8392035. PMID 34441330.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  6. ^ Assi, Mohammed Hussein (July 2023). "Thyroid Gland Basics: A Comprehensive Review". Mustansiriya Medical Journal. 22 (2): 172–181. doi:10.4103/mj.mj_43_23.{{cite journal}}: CS1 maint: unflagged free DOI (link)
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  8. ^ a b c d e Rossi, ED; Adeniran, AJ; Faquin, WC (December 2019). "Pitfalls in Thyroid Cytopathology". Surgical pathology clinics. 12 (4): 865–881. doi:10.1016/j.path.2019.08.001. PMID 31672295.
  9. ^ a b c Thodou, Eleni; Canberk, Sule; Schmitt, Fernando (June 2021). "Challenges in Cytology Specimens With Hürthle Cells". Frontiers in Endocrinology. 12. doi:10.3389/fendo.2021.701877.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  10. ^ Naot, Dorit; Musson, David S.; Cornish, Jillian (January 2019). "The Activity of Peptides of the Calcitonin Family in Bone". Physiological Reviews. 99 (1): 781–805. doi:10.1152/physrev.00066.2017.
  11. ^ a b Fugazzola, Laura (January 2023). "Medullary thyroid cancer - An update". Best Practice & Research Clinical Endocrinology & Metabolism. 37 (1): 101655. doi:10.1016/j.beem.2022.101655.
  12. ^ Gild, Matti L; Clifton-Bligh, Roderick J; Wirth, Lori J; Robinson, Bruce G (September 2023). "Medullary Thyroid Cancer: Updates and Challenges". Endocrine Reviews. 44 (5): 934–946. doi:10.1210/endrev/bnad013.
  13. ^ a b Juhlin, C. Christofer; Baloch, Zubair W. (December 2023). "Pitfalls in Thyroid Fine-Needle Aspiration Cytopathology: An Approach to Atypical Findings". Acta Cytologica. 68 (3): 179–193. doi:10.1159/000535907.
  14. ^ a b Hoang, Van Trung; Trinh, Cong Thao (October 2020). "A Review of the Pathology, Diagnosis and Management of Colloid Goitre". European Endocrinology. 16 (2): 131. doi:10.17925/EE.2020.16.2.131.
  15. ^ a b c Cibas, Edmund S.; Ali, Syed Z. (November 2009). "The Bethesda System for Reporting Thyroid Cytopathology". American Journal of Clinical Pathology. 132 (5): 658–665. doi:10.1309/AJCPPHLWMI3JV4LA. ISSN 0002-9173.
  16. ^ a b c Ali, Syed Z.; VanderLaan, Paul A. (June 2023). "The Bethesda System for Reporting Thyroid Cytopathology". Springer Nature. doi:10.1007/978-3-031-28046-7.
  17. ^ a b c d Nishino, Michiya; Krane, Jeffrey F. (September 2018). "Updates in Thyroid Cytology". Surgical Pathology Clinics. 11 (3): 467–487. doi:10.1016/j.path.2018.05.002. ISSN 1875-9181.
  18. ^ Kadam, P N (January 2020). "Evaluation of Bethesda System for Reporting Thyroid Cytology with Histopathological Correlation". Journal of Medical Science And clinical Research. 08 (01). doi:10.18535/jmscr/v8i1.91. ISSN 2347-176X.
  19. ^ Rossi, Esther Diana; Pantanowitz, Liron; Raffaelli, Marco; Fadda, Guido (June 2021). "Overview of the Ultrasound Classification Systems in the Field of Thyroid Cytology". Cancers. 13 (13): 3133. doi:10.3390/cancers13133133. ISSN 2072-6694.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  20. ^ Horvath, Eleonora; Majlis, Sergio; Rossi, Ricardo; Franco, Carmen; Niedmann, Juan P.; Castro, Alex; Dominguez, Miguel (May 2009). "An Ultrasonogram Reporting System for Thyroid Nodules Stratifying Cancer Risk for Clinical Management". The Journal of Clinical Endocrinology & Metabolism. 94 (5): 1748–1751. doi:10.1210/jc.2008-1724. ISSN 0021-972X.
  21. ^ a b Ohori, N. Paul (November 2024). "Evolving concepts in thyroid cytology". Journal of the American Society of Cytopathology. 13 (6): 389–396. doi:10.1016/j.jasc.2024.08.127.
  22. ^ Delahunty, Ruth (September 2023). "The Role of molecular ThyroSeq V3 testing for diagnosis and management of indeterminate thyroid nodules". International Undergraduate Journal of Health Sciences. 3 (1). doi:10.61862/2811-5937.1055. ISSN 2811-5937.
  23. ^ Kezlarian, Brie; Lin, Oscar (December 2021). "Artificial Intelligence in Thyroid Fine Needle Aspiration Biopsies". Acta Cytologica. 65 (4): 324–329. doi:10.1159/000512097. ISSN 0001-5547. PMC 8491503. PMID 33326953.
  24. ^ Slabaugh, Greg; Beltran, Luis; Rizvi, Hasan; Deloukas, Panos; Marouli, Eirini (November 2023). "Applications of machine and deep learning to thyroid cytology and histopathology: a review". Frontiers in Oncology. 13. doi:10.3389/fonc.2023.958310. ISSN 2234-943X.{{cite journal}}: CS1 maint: unflagged free DOI (link)