Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026
Model Based on Ultrasound Radiomics and Machine Learning for Differentiating Uterine Fibroids and Adenomyomas.
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Wu Qianqi, Deng Yiwen, Chen Li, Li Xia, Qiu Lijuan, Min Wenhui, Cao Shuangyi, Zhou Xin, Yi Fei, Zhang Chunquan
Аннотация
OBJECTIVES: To 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.
METHODS: The 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.
RESULTS: The 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.
CONCLUSIONS: 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.
METHODS: The 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.
RESULTS: The 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.
CONCLUSIONS: 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.