Diffusion-Weighted Imaging for Differentiating Uterine Leiomyosarcoma From Degenerated Leiomyoma
Diffusion-Weighted Imaging for Differentiating Uterine Leiomyosarcoma From Degenerated Leiomyoma
复制标题
弥散加权成像用于区分子宫平滑肌肉瘤和退变平滑肌瘤
DOI：
10.1097/rct.0000000000000565
复制
发表时间：
2017-07-01
影响因子：
1.3
通讯作者：
Ma, FengHua
中科院分区：
文献类型：
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作者：
Li, Hai Ming;Liu, Jia;Ma, FengHua
目的探讨磁共振弥散加权成像（DWI）对子宫平滑肌肉瘤（ULMS）和变性平滑肌瘤（DLM）的鉴别诊断价值。方法经手术病理证实的16例ULMS和26例DLMS患者均行常规MRI和DWI检查。测量两组肿瘤的平均表观扩散系数（ADC）值，并使用独立样本t检验进行比较（分别为B = 0.1000 s/mm 2 [ADC 1]; B = 0.800 s/mm 2 [ADC 2]）。应用受试者工作特征曲线评价DWI在鉴别ULMS和DLM中的诊断性能。使用组内相关系数和Bland-Altman分析评价观察者内和观察者间一致性。结果ULMS的平均ADC值（0.81 ± 0.14 × 10− 3 mm 2/s [ADC 1]，0.90 ± 0.11 × 10− 3 mm 2/s [ADC 2]）显著低于DLMs（1.22 ± 0.22 × 10− 3 mm 2/s [ADC 1]，1.50 ± 0.22 × 10− 3 mm 2/s [ADC 2]）（分别为P < 0.001，<0.001）。表征ULMS的灵敏度、特异性、准确性以及阳性和阴性预测值分别为100%、90%、93%、83%和100% [ADC 1]和100%、93%、96%、90%和100% [ADC 2]。观察者内和观察者间的重现性极好（组内相关系数= 0.967-0.988;变异性小，一致性限为95%）。结论弥散加权成像有助于鉴别ULMS和DLM。
Purpose The study aimed to investigate magnetic resonance diffusion-weighted imaging (DWI) in the differentiation of uterine leiomyosarcoma (ULMS) from degenerated leiomyoma (DLM). Methods Sixteen patients with ULMSs and 26 patients with DLMs confirmed by surgery and pathology underwent conventional magnetic resonance imaging and DWI. The mean apparent diffusion coefficient (ADC) values of the 2 groups’ tumors were measured and compared using an independent-sample t test (b = 0.1000 s/mm2 [ADC1]; b = 0.800 s/mm2 [ADC2], respectively). A receiver operating characteristic curve was used to evaluate the diagnostic performance of DWI in the differentiation of ULMS from DLM. Intraobserver and interobserver agreements were evaluated using an intraclass correlation coefficient and Bland-Altman analysis. Results The mean ADC value in ULMSs (0.81 ± 0.14 × 10−3mm2/s [ADC1], 0.90 ± 0.11 × 10−3mm2/s [ADC2]) was significantly lower than that in DLMs (1.22 ± 0.22 × 10−3mm2/s [ADC1], 1.50 ± 0.22 × 10−3mm2/s [ADC2]) (P < 0.001, <0.001, respectively). The sensitivity, specificity, accuracy, and positive and negative predictive values for characterizing ULMS were 100%, 90%, 93%, and 83% and 100% [ADC1] and 100%, 93%, 96%, and 90% and 100% [ADC2]; respectively. Intraobserver and interobserver reproducibilities were excellent (intraclass correlation coefficient = 0.967–0.988; small variability and 95% limits of agreement). Conclusions Diffusion-weighted imaging is helpful in differentiating ULMS from DLM.