Investigative radiology · 2016
Screening Magnetic Resonance Imaging-Based Prediction Model for Assessing Immediate Therapeutic Response to Magnetic Resonance Imaging-Guided High-Intensity Focused Ultrasound Ablation of Uterine Fibroids.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Kim Young-sun, Lim Hyo Keun, Park Min Jung, Rhim Hyunchul, Jung Sin-Ho, Sohn Insuk, Kim Tae-Joong, Keserci Bilgin
Аннотация
OBJECTIVES: The aim of this study was to fit and validate screening magnetic resonance imaging (MRI)-based prediction models for assessing immediate therapeutic responses of uterine fibroids to MRI-guided high-intensity focused ultrasound (MR-HIFU) ablation.
MATERIALS AND METHODS: Informed consent from all subjects was obtained for our institutional review board-approved study. A total of 240 symptomatic uterine fibroids (mean diameter, 6.9 cm) in 152 women (mean age, 43.3 years) treated with MR-HIFU ablation were retrospectively analyzed (160 fibroids for training, 80 fibroids for validation). Screening MRI parameters (subcutaneous fat thickness [mm], x1; relative peak enhancement [%] in semiquantitative perfusion MRI, x2; T2 signal intensity ratio of fibroid to skeletal muscle, x3) were used to fit prediction models with regard to ablation efficiency (nonperfused volume/treatment cell volume, y1) and ablation quality (grade 1-5, poor to excellent, y2), respectively, using the generalized estimating equation method. Cutoff values for achievement of treatment intent (efficiency >1.0; quality grade 4/5) were determined based on receiver operating characteristic curve analysis. Prediction performances were validated by calculating positive and negative predictive values.
RESULTS: Generalized estimating equation analyses yielded models of y1 = 2.2637 - 0.0415x1 - 0.0011x2 - 0.0772x3 and y2 = 6.8148 - 0.1070x1 - 0.0050x2 - 0.2163x3. Cutoff values were 1.312 for ablation efficiency (area under the curve, 0.7236; sensitivity, 0.6882; specificity, 0.6866) and 4.019 for ablation quality (0.8794; 0.7156; 0.9020). Positive and negative predictive values were 0.917 and 0.500 for ablation efficiency and 0.978 and 0.600 for ablation quality, respectively.
CONCLUSIONS: Screening MRI-based prediction models for assessing immediate therapeutic responses of uterine fibroids to MR-HIFU ablation were fitted and validated, which may reduce the risk of unsuccessful treatment.
MATERIALS AND METHODS: Informed consent from all subjects was obtained for our institutional review board-approved study. A total of 240 symptomatic uterine fibroids (mean diameter, 6.9 cm) in 152 women (mean age, 43.3 years) treated with MR-HIFU ablation were retrospectively analyzed (160 fibroids for training, 80 fibroids for validation). Screening MRI parameters (subcutaneous fat thickness [mm], x1; relative peak enhancement [%] in semiquantitative perfusion MRI, x2; T2 signal intensity ratio of fibroid to skeletal muscle, x3) were used to fit prediction models with regard to ablation efficiency (nonperfused volume/treatment cell volume, y1) and ablation quality (grade 1-5, poor to excellent, y2), respectively, using the generalized estimating equation method. Cutoff values for achievement of treatment intent (efficiency >1.0; quality grade 4/5) were determined based on receiver operating characteristic curve analysis. Prediction performances were validated by calculating positive and negative predictive values.
RESULTS: Generalized estimating equation analyses yielded models of y1 = 2.2637 - 0.0415x1 - 0.0011x2 - 0.0772x3 and y2 = 6.8148 - 0.1070x1 - 0.0050x2 - 0.2163x3. Cutoff values were 1.312 for ablation efficiency (area under the curve, 0.7236; sensitivity, 0.6882; specificity, 0.6866) and 4.019 for ablation quality (0.8794; 0.7156; 0.9020). Positive and negative predictive values were 0.917 and 0.500 for ablation efficiency and 0.978 and 0.600 for ablation quality, respectively.
CONCLUSIONS: Screening MRI-based prediction models for assessing immediate therapeutic responses of uterine fibroids to MR-HIFU ablation were fitted and validated, which may reduce the risk of unsuccessful treatment.