ArticleBMC immunology2025
Prediction of Seronegative Hashimoto's thyroiditis using machine learning models based on ultrasound radiomics: a multicenter study.
Article in BMC immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence-based ultrasound diagnosis of Hashimoto's thyroiditis: a systematic review and meta-analysis.Thyroid research · 2026Review
- Endocrine polyautoimmunity: Mechanistic insights and the future of AI-driven diagnostics.EXCLI journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors.
Funding
Abstract
backgroundSeronegative Hashimoto's thyroiditis is often underdiagnosed due to the lack of antibody markers. Combining ultrasound radiomics with machine learning offers potential for early detection in patients with normal thyroid function.
methodsData from 164 patients with single thyroid lesions and normal thyroid function, treated surgically between 2016 and 2024, were retrospectively collected from four hospitals. Radiomics features were extracted from ultrasound images of non-tumorous hypoechoic areas. Pathological lymphocytic infiltration and hypoechoic ratios were evaluated by senior pathologists and ultrasound physicians. A machine learning model, CCH-NET, was developed using a random forest classifier after feature selection with Least Absolute Shrinkage and Selection Operator (LASSO) regression. The model was trained and tested with an 80:20 split and compared to senior ultrasound physicians.
resultsThe CCH-NET model achieved a sensitivity of 0.762, specificity of 0.714, and an area under the curve (AUC) of 0.8248, outperforming senior ultrasound physicians (AUC = 0.681). It maintained consistent accuracy across test sets, with F1 scores of 0.778 and 0.720 in Test_1 and Test_2, respectively, and exhibited superior predictive rates.
conclusionThe CCH-NET model enhances accuracy in detecting early Seronegative Hashimoto's thyroiditis over senior ultrasound physicians. ETHICS: No. [2023] H013
trial registrationChinese Clinical Trial Registry;CTR2400092179; 12 November 2024.
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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 Socratic graph.