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<p>This study aimed to develop and validate a clinical prediction model for the success of the focused ultrasound ablation system (FUAS) in treating adenomyosis. A retrospective analysis was conducted on 250 patients from Qingdao Women and Children’s Hospital (2019–2022). Patients were categorized into success (<i>n</i> = 108) or failure (<i>n</i> = 142) groups based on a post-treatment lesion ablation rate greater than 80%. The dataset was split into training (70%) and validation (30%) sets. The multivariable analysis identified age (OR = 1.10, <i>P</i> = 0.014), depth of adenomyosis (OR = 1.03, <i>P</i> = 0.036), and uterine body (OR = 0.25, <i>P</i> = 0.027) as independent predictors of FUAS efficacy, which were used to build the model. In the training set, the model achieved an AUC of 0.74 for efficacy prediction. The Bootstrap ROC indicates an AUC mean of 0.751 with a standard deviation of 0.036. In conclusion, a model based on age, depth of ademyosis, and uterine body lesions treatment dose accurately predicts FUAS success for adenomyosis. This tool can aid clinical decision-making and promote personalized treatment.</p>

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Development and validation of a clinical prediction model for the success of focused ultrasound ablation system for the treatment of adenomyosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月2日

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  • Limei Cui,
  • Gang Zhang,
  • Changmei Sang,
  • Zhiyan Wu,
  • Ruoqing Li,
  • Shuping Zhao

摘要

This study aimed to develop and validate a clinical prediction model for the success of the focused ultrasound ablation system (FUAS) in treating adenomyosis. A retrospective analysis was conducted on 250 patients from Qingdao Women and Children’s Hospital (2019–2022). Patients were categorized into success (n = 108) or failure (n = 142) groups based on a post-treatment lesion ablation rate greater than 80%. The dataset was split into training (70%) and validation (30%) sets. The multivariable analysis identified age (OR = 1.10, P = 0.014), depth of adenomyosis (OR = 1.03, P = 0.036), and uterine body (OR = 0.25, P = 0.027) as independent predictors of FUAS efficacy, which were used to build the model. In the training set, the model achieved an AUC of 0.74 for efficacy prediction. The Bootstrap ROC indicates an AUC mean of 0.751 with a standard deviation of 0.036. In conclusion, a model based on age, depth of ademyosis, and uterine body lesions treatment dose accurately predicts FUAS success for adenomyosis. This tool can aid clinical decision-making and promote personalized treatment.