Thyroid cancer: who works on it

One card per institution. Filter by target, by country, or show only institutions in EU-13 (Horizon Europe widening) countries.

• All targets
• BRAF 15
• RET 8
• PI3K 0
• TP53 0
• TERT 0
• MAPK 1
• GLP-1 1
• AKT 0
• PTEN 0
• STAT3 0
• mTOR 0
• PD-1 5
• NTRK 2
• ERK 0
• PSMA 1
• RAS 1
• ALK 1
• EGFR 3
• MEK 1
• VEGF 2
• PD-L1 0
• JAK 0
• CTNNB1 0
• KRAS 1
• NRAS 2
• NRF2 0
• PPAR 0
• TROP2 3
• AMPK 0
• DLL3 2
• HIF-1 0
• PIK3CA 0
• ABL1 0
• DPP-4 0
• IL-6 1
• METTL3 0
• TNF 0
• YAP 0
• AXL 1
• EZH2 0
• FGFR 0
• JNK 0
• MET 1
• MYC 0
• PKM2 0
• ROS1 1
• SGLT2 0
• STING 0
• ATM 0
• BRCA1 0
• BRCA2 0
• BRD4 0
• CGRP 0
• CTLA-4 0
• CXCR4 0
• FAK 0
• HMGB1 0
• IDH 0
• IDO1 0
• NLRP3 0

EU-13 is a filter, not a ranking: it only narrows the list.

No research groups match this view yet.

Trials whose registry record says individual participant data (IPD) will be shared.

No trial in this view states that it shares participant data.

Find publications on this disease by a MeSH method term, for example a sequencing or imaging method.

• Single-Cell Analysis 24
• Organoids 6
• Mendelian Randomization Analysis 16
• CRISPR-Cas Systems 3
• Gene Expression Profiling 65
• Sequence Analysis, RNA 7
• Whole Genome Sequencing 1
• Exome Sequencing 7
• Proteomics 14
• Metabolomics 9
• Mass Spectrometry 7
• Flow Cytometry 6
• Immunohistochemistry 72
• Liquid Biopsy 9
• Circulating Tumor DNA 3
• Xenograft Model Antitumor Assays 57
• Positron-Emission Tomography 44
• Magnetic Resonance Imaging 18
• Machine Learning 72
• Deep Learning 44
• Cohort Studies 118
• Genome-Wide Association Study 16

• Incorporating natural language processing-derived features from pathology reports into machine learning models to predict differentiated thyroid cancer recurrence.

  Front Endocrinol (Lausanne), Oct 2026

• Network pharmacology and machine learning reveal kit as a target of roburic acid in anaplastic thyroid carcinoma: Preliminary in vitro validation.

  PLoS One, Oct 2026

• A genetic algorithm-optimized EWMA control chart integrating machine learning-based risk prediction for multistage thyroid cancer treatment monitoring.

  PLoS One, Oct 2026

• An explainable and leakage-aware machine learning model for early risk stratification of radioiodine-refractory differentiated thyroid cancer: development and single-center geographic external validation.

  Front Endocrinol (Lausanne), Oct 2026

• Machine learning-based prediction of lymph node metastasis in papillary thyroid carcinoma: an interpretable multidimensional model.

  Front Endocrinol (Lausanne), Sept 2026

• Machine learning diagnostic model integrating ultrasound features and immune-inflammatory biomarkers for identifying papillary thyroid carcinoma in patients with Hashimoto's thyroiditis.

  Front Immunol, Sept 2026

• The value of machine learning models in predicting lung metastasis in papillary thyroid carcinoma.

  Front Endocrinol (Lausanne), Sept 2026

• Quantitative Super-Resolution Ultrasound Microvascular Features for Machine Learning-Based Classification of Thyroid Nodules.

  J Vis Exp, Sept 2026

• Interpretable four-class machine learning prediction of initial I-131 therapy responses in differentiated thyroid cancer.

  Ann Med, Sept 2026

• Predicting unfavorable response to initial radioactive iodine therapy in differentiated thyroid cancer: an explainable machine learning and survival analysis approach.

  Front Endocrinol (Lausanne), Sept 2026

See which targets of this disease your abstract mentions, and go to the people working on them.