SynthesisReviews in endocrine & metabolic disorders2024
Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review.
Synthesis in Reviews in endocrine & metabolic disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 17 citations in OpenAlex.
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Machine Learning for diagnosis of malignant thyroid nodules based on thyroid ultrasound: Systematic review and meta-analysis of studies with external datasets.European journal of radiology open · 2026Article
- The Current Research Landscape on Integrating Artificial Intelligence with Ultrasound Imaging for Cancer Diagnosis: A Dual-Database Bibliometric Study.Current medicinal chemistry · 2026Article
- Comparative Analysis of Diagnostic Performance Between Elastography and AI-Based S-Detect for Thyroid Nodule Detection.Diagnostics (Basel, Switzerland) · 2025Article
- The Role of [18F]FDG PET-Based Radiomics and Machine Learning for the Evaluation of Cardiac Sarcoidosis: A Narrative Literature Review.Medicina (Kaunas, Lithuania) · 2025Review
- Impact of Field-of-view Zooming and Segmentation Batches on Radiomics Features Reproducibility and Machine Learning Performance in Thyroid Scintigraphy.Clinical nuclear medicine · 2025Article
- Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.Skeletal radiology · 2025Article
- Multimodal Ultrasound Radiomic Technology for Diagnosing Benign and Malignant Thyroid Nodules of Ti-Rads 4-5: A Multicenter Study.Sensors (Basel, Switzerland) · 2024Article
- Early Recognition of Secondary Asthma Caused by Lower Respiratory Tract Infection in Children Based on Multi-Omics Signature: A Retrospective Cohort Study.International journal of general medicine · 2024Article
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Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the last years growing evidences on the role of radiomics and machine learning (ML) applied to different nuclear medicine imaging modalities for the assessment of thyroid diseases are starting to emerge. The aim of this systematic review was therefore to analyze the diagnostic performances of these technologies in this setting.
methodsA wide literature search of the PubMed/MEDLINE, Scopus and Web of Science databases was made in order to find relevant published articles about the role of radiomics or ML on nuclear medicine imaging for the evaluation of different thyroid diseases.
resultsSeventeen studies were included in the systematic review. Radiomics and ML were applied for assessment of thyroid incidentalomas at
conclusionDespite some intrinsic limitations of radiomics and ML may have affect the results of this review, these technologies seem to have a promising role in the assessment of thyroid diseases. Validation of preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.