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The Thyroid Gland

The Thyroid Gland

The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...

Synthesis and Regulation of Thyroid Hormones

Synthesis and Regulation of Thyroid Hormones

Low blood levels of the thyroid hormones — triiodothyronine (T3) and thyroxine (T4) — signal the hypothalamus to release the thyrotropin-releasing hormone (TRH). TRH then reaches the pituitary gland and stimulates the release of thyroid-stimulating hormone(TSH) into the bloodstream.
Upon reaching the thyroid gland, TSH stimulates the follicular cells' active uptake of iodide ions from the blood. The ions diffuse to the apical surface of the cells and are oxidized to iodine. The...

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder.

Zexin Li¹, Kaiji Yang², Lili Zhang¹

¹Health Care Center, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Shantou 515041, China.

International Journal of Endocrinology

|January 25, 2021

Summary

This summary is machine-generated.

A new gene expression classifier shows high accuracy in distinguishing malignant from benign thyroid nodules. This tool offers a convenient alternative to existing tests for thyroid nodule classification.

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Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Commercial tests for thyroid nodule classification are limited by cost and accessibility.
There is a need for convenient and effective diagnostic tools for indeterminate thyroid nodules.

Purpose of the Study:

To develop a novel gene expression-based classifier for thyroid nodule classification.
To create a tool with improved clinical utility and accessibility.

Main Methods:

Utilized gene expression data from public databases for thyroid nodule tissues.
Constructed a classifier using immune-related genes and a stacked denoising sparse autoencoder.

Main Results:

The classifier demonstrated strong performance in differentiating malignant and benign thyroid nodules.
Achieved an area under the curve of 0.785, with 92.9% accuracy and high sensitivity (98.6%).
Showcased excellent negative predictive value (98-99%) within a 20-40% cancer prevalence range.

Conclusions:

The developed classifier exhibits excellent discriminative capability for thyroid nodules.
Further validation in cytologically indeterminate thyroid nodules is required prior to clinical implementation.