ReviewJournal of clinical ultrasound : JCU2026
Ultrasound Radiomics for Hashimoto's Thyroiditis: State of the Art and Future Perspectives.
Review in Journal of clinical ultrasound : JCU, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ultrasound radiomics is gradually emerging as a powerful tool for improving the diagnosis and management of Hashimoto's thyroiditis (HT), effectively overcoming the limitations of conventional ultrasound, such as high interobserver variability and difficulties in evaluating nodule characteristics. This narrative review aims to systematically summarize the fundamental principles, algorithms, and clinical application progress of ultrasound radiomics in HT, focusing on its diagnostic value in early diagnosis of HT, assessment of inflammatory activity, differentiation of benign and malignant nodules, and prediction of lymph node metastasis. Meanwhile, this review analyzes the current challenges, including data heterogeneity and insufficient model interpretability. Future directions are also prospected, including the establishment of standardized databases and interpretable artificial intelligence models to support the precise diagnosis and treatment of HT.
Indexed as
Identifiers
What OpenQuestion holds
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.