Thyroid

From Guidelines to Intelligence: How AI Refines Thyroid Nodule Biopsy Decisions.

OBJECTIVE: To evaluate the value of combining American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) with the Demetics ultrasound diagnostic system in reducing the rate of fine-needle aspiration (FNA) biopsies for thyroid nodules.

Multi-Omics-Based Characterization of DNA Methylation Episignatures for Papillary Thyroid Cancer in a Chinese Population.

Thyroid cancer is the most common endocrine malignancy, with papillary thyroid cancer (PTC) accounting for ∼80% of all cases. DNA methylation alterations and gene expression changes in cancer, offer valuable insights into tumor biology and serve as potential clinical biomarkers. However, the functional implications of DNA methylation changes in PTC patients, particularly based on multiomics analy...

Combining structural equation modeling analysis with machine learning for early malignancy detection in Bethesda Category III thyroid nodules.

Atypia of Undetermined Significance (AUS), classified as Category III in the Bethesda Thyroid Cytopathology Reporting System, presents significant dia...

Integrative transcriptomics and single-cell transcriptomics analyses reveal potential biomarkers and mechanisms of action in papillary thyroid carcinoma.

OBJECTIVE: Papillary thyroid carcinoma (PTC) has a high recurrence rate and lacks reliable diagnostic biomarkers. This study aims to identify robust t...

A protein-based classifier for differentiating follicular thyroid adenoma and carcinoma.

Differentiating follicular thyroid adenoma (FTA) from carcinoma (FTC) remains challenging due to similar histological features separate from invasion....

Explainable Deep Learning System for Automatic Detection of Thyroid Eye Disease using Facial Images.

PURPOSE: To report an explainable deep learning (XDL) system to automatically detect thyroid eye disease (TED) using facial images.

AI-based multimodal prediction of lymph node metastasis and capsular invasion in cT1N0M0 papillary thyroid carcinoma.

BACKGROUND: Accurate preoperative evaluation of cT1N0M0 papillary thyroid carcinoma (PTC) is essential for guiding appropriate treatment strategies. A...

Deep learning model for malignancy prediction of TI-RADS 4 thyroid nodules with high-risk characteristics using multimodal ultrasound: A multicentre study.

The automatic screening of thyroid nodules using computer-aided diagnosis holds great promise in reducing missed and misdiagnosed cases in clinical pr...

Extended Linear Confined Zipper Cascaded Reaction for Highly Efficient Intracellular Imaging and Assisting Diagnosis of Thyroid Cancer.

Highly sensitive detection and in situ tracing analysis of small-molecule biomarkers are particularly indispensable to deciphering the pathogenesis an...

Proteomic analysis reveals modulation of key proteins in follicular thyroid cancer progression.

BACKGROUND: Cytopathology cannot be used to reliably distinguish follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC), the second ...

An Ultrasound Image-Based Deep Learning Radiomics Nomogram for Differentiating Between Benign and Malignant Indeterminate Cytology (Bethesda III) Thyroid Nodules: A Retrospective Study.

RATIONALE AND OBJECTIVES: Our objective is to develop and validate a deep learning radiomics nomogram (DLRN) based on preoperative ultrasound images a...

Interobserver agreement between artificial intelligence models in the thyroid imaging and reporting data system (TIRADS) assessment of thyroid nodules.

BACKGROUND: As ultrasound (US) is the most accurate tool for assessing the thyroid nodule (TN) risk of malignancy (RoM), international societies have ...

Impact of thyroglobulin changes on clinical outcomes of differentiated thyroid cancer with biochemical incomplete response.

PURPOSE: Partial patients with biochemical incomplete response (BIR) after initial therapy for differentiated thyroid cancer (DTC) may progress to str...

Effective data selection via deep learning processes and corresponding learning strategies in ultrasound image classification.

In this study, we propose a novel approach to enhancing transfer learning by optimizing data selection through deep learning techniques and correspond...

The impact of a simple positioning aid device on the diagnostic performance of thyroid cancer in CT scans: a randomized controlled trial.

OBJECTIVE: To evaluate the effectiveness of a simple positioning aid device in neck CT scans for the diagnosis of thyroid cancer, with a focus on its ...

Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.

OBJECTIVES: To develop and validate a machine learning (ML) model to differentiate malignant from benign thyroid nodules (TNs) based on the routine da...

Beyond genomics: artificial intelligence-powered diagnostics for indeterminate thyroid nodules-a systematic review and meta-analysis.

INTRODUCTION: In recent years, artificial intelligence (AI) tools have become widely studied for thyroid ultrasonography (USG) classification. The rea...

Multimodal GPT model for assisting thyroid nodule diagnosis and management.

Although using artificial intelligence (AI) to analyze ultrasound images is a promising approach to assessing thyroid nodule risks, traditional AI mod...

Development of an activity-based ratiometric electrochemical substrate for measuring circulating dipeptidyl peptidase-IV/CD26 in whole blood samples.

Dipeptidyl peptidase-IV (DPP-IV) is a circulating blood biomarker that diagnose pancreatic and thyroid cancers, as well as type 2 diabetes. Although c...

Construction of a Multimodal Machine Learning Model for Papillary Thyroid Carcinoma Based on Pathomics and Ultrasound Radiomics Dataset.

The use of machine learning to integrate and analyse multimodal information has broad prospects for enhancing the precision of tumour diagnosis. Our s...