Department of Medical Physics, Turku University Hospital, Turku, Finland.
Department of Radiology, Turku University Hospital, Turku, Finland.
Int J Hyperthermia. 2021;38(1):85-94. doi: 10.1080/02656736.2021.1874545.
To investigate the feasibility of using an apparent diffusion coefficient (ADC) classification in predicting the technical outcome of magnetic resonance imaging-guided high-intensity focused ultrasound (MRgHIFU) treatment of symptomatic uterine fibroids and to compare it to the Funaki classification.
Forty-two patients with forty-eight uterine fibroids underwent diffusion-weighted imaging (DWI) before MRgHIFU treatment. The DW images were acquired with five different b-values. Correlations between ADC values and treatment parameters were assessed. Optimal ADC cutoff values were determined to predict technical outcomes, that is, nonperfused volume ratios (NPVr) such that three classification groups were created (NPVr of <30%, 30-80%, or >80%). Results were compared to the Funaki classification using receiver-operating-characteristic (ROC) curve analysis, with statistical significance being tested with the Chi-square test.
A statistically significant negative correlation (Spearman's = -0.31, -value < 0.05) was detected between ADC values and NPV ratios. ROC curve analysis indicated that optimal ADC cutoff values of 980 × 10mm/s (NPVr > 80%) and 1800 × 10mm/s (NPVr < 30%) made it possible to classify fibroids into three groups: ADC I (NPVr > 80%), ADC II (NPVr 30-80%) and ADC III (NPVr < 30%). Analysis of the whole model area under the curve resulted in values of 0.79 for the ADC classification (-value = 0.0007) and 0.62 for the Funaki classification (-value = 0.0527).
Lower ADC values prior to treatment correlate with higher NPV ratios. The ADC classification seems to be able to predict the NPV ratio and may even outperform the Funaki classification. Based on these results DWI and ADC maps should be included in the MRI screening protocol.
探讨表观扩散系数(ADC)分类在预测磁共振引导高强度聚焦超声(MRgHIFU)治疗症状性子宫肌瘤技术效果中的可行性,并与 Funaki 分类进行比较。
42 例 48 个子宫肌瘤患者在 MRgHIFU 治疗前进行弥散加权成像(DWI)。DW 图像采用 5 种不同的 b 值采集。评估 ADC 值与治疗参数之间的相关性。确定最佳 ADC 截断值以预测技术结果,即无灌注体积比(NPVr),并创建三个分类组(NPVr<30%、30-80%或>80%)。使用受试者工作特征(ROC)曲线分析将结果与 Funaki 分类进行比较,并使用卡方检验测试统计学意义。
ADC 值与 NPV 比之间存在统计学显著负相关(Spearman's =-0.31,-值<0.05)。ROC 曲线分析表明,980×10mm/s(NPVr>80%)和 1800×10mm/s(NPVr<30%)的最佳 ADC 截断值可将肌瘤分为三组:ADC I(NPVr>80%)、ADC II(NPVr 30-80%)和 ADC III(NPVr<30%)。整个模型的曲线下面积分析得出,ADC 分类的曲线下面积为 0.79(-值=0.0007),Funaki 分类的曲线下面积为 0.62(-值=0.0527)。
治疗前较低的 ADC 值与较高的 NPV 比值相关。ADC 分类似乎能够预测 NPV 比值,甚至可能优于 Funaki 分类。基于这些结果,DWI 和 ADC 图应纳入 MRI 筛查方案。