Thyroid

Evaluation of 2D and 3D nnU-Net models with two-label and three-label strategies for automatic segmentation and total metabolic tumor volume estimation of metastatic differentiated thyroid carcinoma on FDG-PET/CT.

PURPOSE: To evaluate the segmentation performance and total metabolic tumor volume (TMTV) prediction accuracy of 2D and 3D nnU-Net models under two-label and three-label strategies for metastatic differentiated thyroid carcinoma (DTC) on FDG PET/CT images. MATERIALS AND METHODS: A total of 194 patients with FDG-avid metastatic DTC who underwent PET/CT prior to iodine-131 treatment (2009-2022) were...

Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.

BACKGROUND: Explainable artificial intelligence (xAI) is increasingly used in medical imaging to enhance transparency, clinical interpretability, and trust in artificial intelligence (AI)-assisted diagnostics, particularly in oncology. Evidence on how explainability is implemented, validated, and reported in cancer imaging remains fragmented. OBJECTIVE: This scoping review aimed to systematically ...

Thyroid disease detection using enhanced extreme learning machine based on drop-connect method.

The long-term physiologic effects of thyroid problems make them one of the most important endocrine disorders. Even if a lot of machine learning and d...

Preliminary machine-learning model with clinical, US, and CEUS features for the diagnosis of thyroid follicular-patterned lesions.

BACKGROUND: Though follicular thyroid carcinoma can be confirmed postoperatively by the histological findings of capsular or vascular invasion, preope...

Metabolomic profiling of exhaled breath condensate identifies a novel biomarker panel for detecting thyroid and breast cancer.

BACKGROUND: The non-invasive detection of cancer via exhaled breath condensate (EBC) represents a promising frontier in oncology. This study explores ...

Evaluating the diagnostic performance of multimodal large language models in thyroid cytology.

Thyroid fine-needle aspiration (FNA) cytology is a widely used, cost-effective method for evaluating thyroid nodules. However, interpretation of thyro...

Remission, Relapse, and Thyroid-Related Parameters Measurement at Discontinuation of Low-Dose Anti-Thyroid Drugs in Graves' Disease.

BACKGROUND: This study aimed to elucidate the relationship between thyroid-related parameters and the prognosis of Graves' disease (GD). METHODS: This...

Transcriptomic sequencing analysis of the tumor microenvironment atlas and potential mechanisms of metastasis in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is the most prevalent thyroid malignancy and its incidence continues to rise. Although prognosis is generally favora...

Prediction of transient hypocalcemia following total thyroidectomy using machine learning methods.

Combining single cell and bulk transcriptomics with Mendelian randomization identifies gut microbiota related prognostic genes and mechanisms in thyroid cancer.

BACKGROUND: Imbalance in gut microbiota (GM) may play a role in the development of thyroid cancer (TC), but the specific mechanisms remain unclear. Th...

Comparative analysis of large language models and clinicians in thyroid eye disease using structured questionnaires.

To preliminarily evaluate agreement between large language models (LLMs) and ophthalmic clinicians regarding thyroid eye disease (TED) clinical decisi...

Mapping the Research Landscape and Emerging Trends in Hashimoto's Encephalopathy: A CiteSpace-Based Visualization Analysis.

INTRODUCTION: Hashimoto's thyroiditis is the leading cause of hypothyroidism in iodine-sufficient regions and is often accompanied by various comorbid...

Artificial intelligence for predicting transient hypocalcemia after total thyroidectomy.

Transient hypocalcemia is a common complication of total thyroidectomy. This study aimed to evaluate whether machine learning (ML)-based models could ...

Integrated multi-omics and single-cell analyses identify metabolic heterogeneity and therapeutic vulnerabilities in medullary thyroid cancer.

BACKGROUND: Medullary thyroid cancer (MTC) is a heterogeneous and aggressive malignancy with limited therapeutic options. Metabolic reprogramming, a h...

Multiple machine learning models for the prediction of an early response to radioactive iodine therapy in hyperthyroidism: ablative dose concept.

BACKGROUND: The aim of this novel study was to explore the possibility of the development of a machine learning (ML) model that can predict early resp...

MRI-based automated segmentation and multiparametric quantification for assessing orbital soft tissue involvement in thyroid eye disease.

OBJECTIVES: To develop a deep learning-based multimodal framework for automated segmentation of orbital soft tissues and identify quantitative imaging...

Early volume reduction slope as a novel predictor of long-term outcomes following microwave ablation of benign thyroid nodules: a machine learning-enhanced prediction model.

OBJECTIVE: This study aimed to develop and validate a predictive model incorporating early VRR slope kinetics to predict long-term treatment outcomes....

Improving predicted risk of recurrence using molecular profiling in papillary thyroid cancer.

Molecular testing can refine the prediction of cancer recurrence. We sought to compare patterns of gene expression in patients with and without recurr...

A lightweight GhostYOLO framework with GhostDynamic Module and multi-scale attention for thyroid nodule diagnosis.

Ultrasound is the primary tool for thyroid nodule assessment, but diagnostic accuracy and efficiency require improvement. This study aims to develop a...

Precision Medicine in Autoimmune Thyroid Diseases: Decoding Biomarkers, Targeted Therapies and Digital Innovation.

INTRODUCTION: Autoimmune thyroid diseases (AITDs), including Graves' disease and Hashimoto's thyroiditis, arise from genetic, epigenetic, and environm...