Abstract：
UNASSIGNED: To develop a novel scoring system based on magnetic resonance imaging (MRI) for predicting the difficulty of ultrasound-guided high-intensity focused ultrasound (USgHIFU) ablation for uterine fibroids.
UNASSIGNED: A total of 637 patients with uterine fibroids were enrolled. Sonication time, non-perfused volume ratio (NPVR), and ultrasound energy delivered for ablating 1 mm3 of fibroid tissue volume (E/V) were each classified as three levels and assigned scores from 0 to 2, respectively. Treatment difficulty level was then assessed by adding up the scores of sonication time, NPVR and E/V for each patient. The patients with score lower than 3 were categorized into low difficulty group, with score equal to or greater than 3 were categorized into high difficulty group. The potential predictors for treatment difficulty were compared between the two groups. Multifactorial logistic regression analysis model was created by analyzing the variables. The difficulty score system was developed using the beta coefficients of the logistic model.
UNASSIGNED: Signal intensity on T2WI, fibroid location index, largest diameter of fibroids, abdominal wall thickness, homogeneity of the signal of fibroids, and uterine position were independent influencing factors for the difficulty of USgHIFU for uterine fibroids. A prediction equation was obtained: difficulty score = 17 × uterine position (anteverted =0, retroverted =1)+71 × signal intensity (hypointense = 0, isointense/hyperintense = 1) +8 × enhancement (homogenous = 0, heterogeneous = 1)+25×(largest diameter of fibroids-20) +35 × (fibroid location index -0.2) +1×(abdominal wall thickness -5).
UNASSIGNED: This scoring system established based on MRI findings can be used to reliably predict the difficulty level of USgHIFU treatment of uterine fibroids.
UNASSIGNED: A total of 637 patients with uterine fibroids were enrolled. Sonication time, non-perfused volume ratio (NPVR), and ultrasound energy delivered for ablating 1 mm3 of fibroid tissue volume (E/V) were each classified as three levels and assigned scores from 0 to 2, respectively. Treatment difficulty level was then assessed by adding up the scores of sonication time, NPVR and E/V for each patient. The patients with score lower than 3 were categorized into low difficulty group, with score equal to or greater than 3 were categorized into high difficulty group. The potential predictors for treatment difficulty were compared between the two groups. Multifactorial logistic regression analysis model was created by analyzing the variables. The difficulty score system was developed using the beta coefficients of the logistic model.
UNASSIGNED: Signal intensity on T2WI, fibroid location index, largest diameter of fibroids, abdominal wall thickness, homogeneity of the signal of fibroids, and uterine position were independent influencing factors for the difficulty of USgHIFU for uterine fibroids. A prediction equation was obtained: difficulty score = 17 × uterine position (anteverted =0, retroverted =1)+71 × signal intensity (hypointense = 0, isointense/hyperintense = 1) +8 × enhancement (homogenous = 0, heterogeneous = 1)+25×(largest diameter of fibroids-20) +35 × (fibroid location index -0.2) +1×(abdominal wall thickness -5).
UNASSIGNED: This scoring system established based on MRI findings can be used to reliably predict the difficulty level of USgHIFU treatment of uterine fibroids.
摘要：
■开发一种基于磁共振成像（MRI）的新型评分系统，用于预测超声引导下高强度聚焦超声（USgHIFU）消融子宫肌瘤的难度。
■共纳入637例子宫肌瘤患者。声波处理时间,非灌注体积比(NPVR)，和用于消融1mm3纤维瘤组织体积（E/V）的超声能量分别分为三个级别，并分别从0到2分。然后通过将超声处理时间的分数相加来评估治疗难度水平，每位患者的NPVR和E/V。得分低于3分的患者分为低难度组，得分等于或大于3分为高难度组。比较两组患者治疗难度的潜在预测因素。通过分析变量建立多因素logistic回归分析模型。难度评分系统是使用逻辑模型的β系数开发的。
■T2WI上的信号强度，肌瘤位置指数,最大直径的肌瘤，腹壁厚度,肌瘤信号的同质性，子宫位置是子宫肌瘤USgHIFU难行的独立影响因素。得到预测方程:难度分数=17×子宫位置(前反转=0，后反转=1)+71×信号强度(低信号=0，等强度/高信号=1)+8×增强(均质=0，异质=1)+25×(肌瘤最大直径-20)+35×(肌瘤位置指数-0.2)+1×(腹壁厚度-5)。
■这种根据MRI发现而建立的评分系统可用于可靠地预测子宫肌瘤的USgHIFU治疗难度水平。
■共纳入637例子宫肌瘤患者。声波处理时间,非灌注体积比(NPVR)，和用于消融1mm3纤维瘤组织体积（E/V）的超声能量分别分为三个级别，并分别从0到2分。然后通过将超声处理时间的分数相加来评估治疗难度水平，每位患者的NPVR和E/V。得分低于3分的患者分为低难度组，得分等于或大于3分为高难度组。比较两组患者治疗难度的潜在预测因素。通过分析变量建立多因素logistic回归分析模型。难度评分系统是使用逻辑模型的β系数开发的。
■T2WI上的信号强度，肌瘤位置指数,最大直径的肌瘤，腹壁厚度,肌瘤信号的同质性，子宫位置是子宫肌瘤USgHIFU难行的独立影响因素。得到预测方程:难度分数=17×子宫位置(前反转=0，后反转=1)+71×信号强度(低信号=0，等强度/高信号=1)+8×增强(均质=0，异质=1)+25×(肌瘤最大直径-20)+35×(肌瘤位置指数-0.2)+1×(腹壁厚度-5)。
■这种根据MRI发现而建立的评分系统可用于可靠地预测子宫肌瘤的USgHIFU治疗难度水平。
