International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group · 2026
MRI radiomics combined with clinical features for predicting initial dose in focused ultrasound ablation surgery for uterine fibroids.
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Zhao Lina, Li Guangbo, Chen Qingyue, He Min, Zhang Zongjiu
Аннотация
OBJECTIVE: To develop and validate a radiomics-clinical model for individualized prediction of the initial treatment dose in focused ultrasound ablation surgery (FUAS) for uterine fibroids.
METHODS: This retrospective study included 210 patients with solitary uterine fibroids who underwent FUAS and were randomly divided into training and testing cohorts (7:3). The outcome variable was defined as the total sonication energy delivered to the initial treatment layer. Regions of interest were manually delineated on preoperative T2-weighted MRI, and radiomics features were extracted using PyRadiomics. After feature selection, multiple machine-learning algorithms were evaluated, and the optimal radiomics model was combined with independent clinical predictors to construct a radiomics-clinical model. Model performance was assessed using receiver-operating-characteristic analysis and decision curve analysis (DCA).
RESULTS: A total of 1,223 radiomics features were extracted, of which 10 were retained for model construction. Body mass index (BMI), rectus abdominis thickness, and preoperative surgical score were identified as independent clinical predictors. The combined radiomics-clinical model demonstrated the best predictive performance, with an AUC of 0.862 (95% CI, 0.821-0.898) in the training cohort and 0.803 (95% CI, 0.756-0.845) in the testing cohort. DCA demonstrated greater clinical net benefit for the combined model compared with either the radiomics or clinical model alone.
CONCLUSIONS: MRI-based radiomics combined with clinical variables may provide a useful approach for individualized prediction of the initial FUAS treatment dose for uterine fibroids.
METHODS: This retrospective study included 210 patients with solitary uterine fibroids who underwent FUAS and were randomly divided into training and testing cohorts (7:3). The outcome variable was defined as the total sonication energy delivered to the initial treatment layer. Regions of interest were manually delineated on preoperative T2-weighted MRI, and radiomics features were extracted using PyRadiomics. After feature selection, multiple machine-learning algorithms were evaluated, and the optimal radiomics model was combined with independent clinical predictors to construct a radiomics-clinical model. Model performance was assessed using receiver-operating-characteristic analysis and decision curve analysis (DCA).
RESULTS: A total of 1,223 radiomics features were extracted, of which 10 were retained for model construction. Body mass index (BMI), rectus abdominis thickness, and preoperative surgical score were identified as independent clinical predictors. The combined radiomics-clinical model demonstrated the best predictive performance, with an AUC of 0.862 (95% CI, 0.821-0.898) in the training cohort and 0.803 (95% CI, 0.756-0.845) in the testing cohort. DCA demonstrated greater clinical net benefit for the combined model compared with either the radiomics or clinical model alone.
CONCLUSIONS: MRI-based radiomics combined with clinical variables may provide a useful approach for individualized prediction of the initial FUAS treatment dose for uterine fibroids.