Model Based on Ultrasound Radiomics and Machine Learning for Differentiating Uterine Fibroids and Adenomyomas

Qianqi Wu; Yiwen Deng; Li Chen; Xia Li; Lijuan Qiu; Wenhui Min; Shixun Cao; Xin Zhou; Fei Yi; Chunquan Zhang

doi:10.1002/jum.70253

April 10, 2026Journal of Ultrasound in Medicine

Model Based on Ultrasound Radiomics and Machine Learning for Differentiating Uterine Fibroids and Adenomyomas

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Key Points

To assess the feasibility of an ultrasound radiomics model using machine learning for differentiating uterine fibroids from adenomyomas.
Developed a model using ultrasound radiomics and support vector machine (SVM) analysis.
Evaluated the model's effectiveness in differentiating between uterine fibroids and adenomyomas.
Assessed the model's reliability as a noninvasive tool for clinical use.
The ultrasound radiomics model effectively differentiated uterine fibroids from adenomyomas.
The combined model demonstrated both reliability and effectiveness for clinical applications.

Implication

This model identifies uterine fibroids and adenomyomas, suggesting a reliable tool for clinicians' preoperative planning.

Abstract

Ultrasound 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.

Discussion

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Cite This Study

Wu et al. (2026) studied this question.

synapsesocial.com/papers/69db38274fe01fead37c65e9 https://doi.org/10.1002/jum.70253

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Model Based on Ultrasound Radiomics and Machine Learning for Differentiating Uterine Fibroids and Adenomyomas

Key Points

Implication

Abstract

Discussion

Cite This Study

Model Based on Ultrasound Radiomics and Machine Learning for Differentiating Uterine Fibroids and Adenomyomas

Key Points

Implication

Abstract

Discussion

Cite This Study

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