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June 4, 2026Scientific Reports0 citationsOpen Access

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

LCL CuiGZGang ZhangCSChangmei Sang

Key Points

  • The aim is to create and validate a predictive model that determines the success of focused ultrasound ablation for adenomyosis.
  • Retrospective analysis of 250 patients from Qingdao Women and Children’s Hospital (2019–2022).
  • Patients were categorized into success (n=108) and failure (n=142) groups based on ablation rates.
  • Multivariable analysis identified independent predictors including age, depth of adenomyosis, and uterine body.
  • The model achieved an AUC of 0.74 for predicting efficacy in the training set.
  • Bootstrap ROC indicates an average AUC of 0.751 with a standard deviation of 0.036.
  • Independent predictors included 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).

Abstract

Abstract 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.

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Cite This Study

Cui et al. (2026) studied this question.

synapsesocial.com/papers/6a211689d499ed480b16f72bhttps://doi.org/10.1038/s41598-026-48587-z
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