Thyroid

Multimodal ultrasound and artificial intelligence for characterization of thyroid nodules in Hashimoto's thyroiditis: current challenges and future perspectives.

Patients with Hashimoto's thyroiditis (HT) frequently present with concurrent nodular lesions such as nodular goiter and thyroid cancer (especially papillary thyroid carcinoma, PTC), and their risk of PTC is significantly higher than that of non-HT individuals. Patients with HT exhibit varying degrees of glandular fibrosis, leading to some areas having a "nodular-like" appearance. Some of these no...

Machine Learning Prediction of Recurrence in Pediatric Thyroid Cancer: Malignant Endocrine Tumors Cohort Analysis Using XGBoost and SHAP.

CONTEXT: Pediatric differentiated thyroid carcinoma (DTC) often presents with advanced disease but generally has excellent long-term survival. However, recurrence or failure to achieve remission remains relatively frequent, underscoring the need for improved early risk stratification. OBJECTIVE: To develop and evaluate an interpretable machine learning model for predicting recurrence or nonremissi...

Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

The ever-increasing need for effective therapeutic management of thyroid cancer (TC) necessitates the exploration of novel approaches for advanced dru...

R-GAT: cancer document classification leveraging graph-based residual network for scenarios with limited data.

Accurate classification of cancer-related biomedical abstracts is critical for advancing cancer informatics and supporting decision-making in healthca...

Comparable survival after lobectomy and total thyroidectomy for T3b differentiated thyroid carcinoma: A population-based study.

BACKGROUND: The 2025 American Thyroid Association guidelines recommend total thyroidectomy for all T3b differentiated thyroid carcinoma (DTC). However...

A novel automated parathyroid glands detection and segmentation method in thyroidectomy.

BACKGROUND: Intraoperative preservation of parathyroid glands (PGs) remained a significant challenge in thyroidectomy. Recently, deep learning has dem...

Interpretable machine learning based on intratumoral and peritumoral ultrasound radiomics for predicting central lymph node metastasis in papillary thyroid carcinoma.

OBJECTIVES: This retrospective and single-center study aimed to develop machine learning (ML) model integrating clinical features, ultrasound (US) fea...

Training the diagnostic artificial intelligence in thyroid sonography: how well is deep learning truly learning?

INTRODUCTION: The use of artificial intelligence (AI) for TI-RADS classification in neck sonography for the assessment of thyroid nodules has been pro...

Multidisciplinary Team Diagnosis and Treatment of well-differentiated thyroid carcinoma: Current Landscape and Future Prospects.

BACKGROUND: The incidence of thyroid cancer has increased markedly in recent years, largely driven by well-differentiated thyroid carcinoma (WDTC). WD...

Interpretable Machine Learning Model Using Digitized US Features for Classifying Complex Thyroid Nodules.

Purpose To develop a digitized integrated feature-based interpretable machine learning classification model to accurately recognize complex thyroid no...

Improving Diagnostic Precision in Thyroid Pathology by Synergistic Use of AI and Molecular Markers.

OBJECTIVES: For indeterminate thyroid nodules, molecular tests offer high negative predictive value (NPV), reducing missed malignancies, but have limi...

Machine learning-based classification of papillary thyroid carcinoma versus multinodular goiter using preoperative laboratory and cytology data.

A Deep Learning Framework for Predicting Teprotumumab Treatment Response in Thyroid Eye Disease.

PURPOSE: To develop and evaluate a deep learning-based framework for quantifying thyroid eye disease (TED) severity before and after teprotumumab trea...

Mechanism Based Hierarchical Machine Learning for High-Throughput Quantitative Prediction of Estrogenic, Androgenic, and Thyroid Disruption Activities.

Although qualitative predictions of endocrine-disrupting chemicals (EDCs) are well established, quantitative high-throughput models remain underdevelo...

Combined Modeling Approaches for Assessing Sodium-Iodide Symporter Inhibition.

The sodium-iodide symporter (NIS, SLC5A5) plays a crucial role in thyroid hormone synthesis. Especially during brain development, correct thyroid sign...

Machine learning-based prediction of central lymph node metastasis in unifocal papillary thyroid microcarcinoma.

OBJECTIVE: This study aims to develop a machine learning (ML) model to predict the risk of central lymph node metastasis (CLNM) in patients with papil...

ProteoAutoNet: high-throughput co-eluted protein analysis with robotics and machine learning.

Co-fractionation mass spectrometry (CF-MS) enables large-scale profiling of endogenous protein-protein interactions, yet CF-MS data generation is of l...

Artificial Intelligence Applications in Thyroid Cancer Care.

CONTEXT: Artificial intelligence (AI) has created tremendous opportunities to improve thyroid cancer care. EVIDENCE ACQUISITION: We used the "artifici...

[Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning].

OBJECTIVES: To develop and validate a multimodal radiomics model based on machine learning for predicting central lymph node metastasis (CLNM) in pati...

Combined effects of persistent organic pollutants and endocrine-disrupting chemicals on thyroid disease risk: A WQS-XGBoost analysis of US population data.

To assess the combined effects of mixed persistent organic pollutants and endocrine-disrupting chemicals on thyroid disease risk, we analyzed data fro...