ArticleJournal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine2026
Model Based on Ultrasound Radiomics and Machine Learning for Differentiating Uterine Fibroids and Adenomyomas.
Article in Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Enhancing the Clinical Applicability of Ultrasound Radiomics for Differentiating Uterine Fibroids and Adenomyomas.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
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10 authors.
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Abstract
objectivesTo evaluate the value of radiomics based on ultrasound in differentiating uterine fibroids (UFs) and uterine adenomyomas (AMs) and construct a noninvasive tool for this differentiation.
methodsThe clinical data and ultrasound images of 659 patients diagnosed with UFs or AMs postoperatively were retrospectively analyzed at 3 institutions from January 2020 to December 2024. The cohort comprised patients with 422 UFs and 237 uterine AMs, divided into training (Institution 1 and Institution 2) and external test cohort (Institution 3). Radiomics features extracted from ultrasound images were used to develop models based on different machine learning classifiers. A combined model was developed combining radiomics features with clinical characteristics and a nomogram was depicted. The performance of the models was assessed by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve.
resultsThe clinical model demonstrated moderate performance with AUCs of 0.767 (95% CI: 0.7259-0.8082) in the training cohort and 0.747 (95% CI: 0.6003-0.8933) in the test cohort. The radiomics model based on the support vector machine (SVM) showed the best performance in discriminating UFs and AMs, with AUCs of 0.899 (95% CI: 0.8721-0.9256) in the training cohort and 0.823 (95% CI: 0.7297-0.9159) in the external test cohort, respectively. The combined model presented better efficacy compared with the clinical model and the radiomics model, with AUCs of 0.923 (95% CI: 0.9015-0.9451) and 0.894 (95% CI: 0.8285-0.9596) in the training and external test cohorts, respectively. The calibration curves suggested good consistency and decision curves showed the highest overall clinical benefit for the combined model.
conclusionsUltrasound radiomics model based on SVM is feasible to differentiate UFs and AMs, and the combined model is a reliable and effective noninvasive tool for differentiation between UFs and AMs, which can assist clinicians in preoperative planning and fertility preservation strategies.
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