Department of Radiology, Hyogo Medical University, Nishinomiya, Japan.
Department of Radiological Technology, Hyogo Medical University Hospital, Nishinomiya, Japan.
In Vivo. 2022 Nov-Dec;36(6):2790-2799. doi: 10.21873/invivo.13016.
BACKGROUND/AIM: This study was conducted to ascertain the optimal combination of non-contrast magnetic resonance (MR) imaging sequences for the differential diagnosis between small angiomyolipoma (AML) with minimal fat and clear cell renal cell carcinoma (CCRCC).
Thirty-nine patients with pathologically proven AML with minimal fat (n=6) or CCRCC (n=33) measuring 4 cm or less were included. All underwent MR imaging before partial nephrectomy or percutaneous biopsy. Four quantitative parameters of tumors were evaluated: signal intensity (SI) index of T1W- gradient-echo imaging, SI index of T2- fat suppression imaging (T2-SI index), apparent diffusion coefficient (ADC) value, and standard deviation (SD) of ADC. These quantitative parameters were compared using Wilcoxon rank-sum test and receiver operating characteristic (ROC) curve analyses. The optimal combination of quantitative parameters was sought using logistic regression analysis.
Comparison of quantitative parameters showed that the T2-SI index (median, AML with minimal fat vs. CCRCC; 0.74 vs. 1.27, p<0.001), ADC value (1.12 vs. 1.75, p=0.005), and SD of ADC (104 vs. 233, p<0.001) were significantly lower in AML with minimal fat than CCRCC. From the ROC curve analysis, the highest area under the curve (1.000; 100% sensitivity; 100% specificity) was obtained using the logistic regression model with the SD of ADC and T2-SI index or ADC value as explanatory variables.
SD of ADC combined with T2-SI index or ADC value exhibited the highest diagnostic performance for differentiating small AML with minimal fat from CCRCC.
背景/目的:本研究旨在确定非对比磁共振成像(MR)序列的最佳组合,以对小血管平滑肌脂肪瘤(AML)伴微小脂肪和透明细胞肾细胞癌(CCRCC)进行鉴别诊断。
纳入 39 名经病理证实的直径≤4cm 的小 AML 伴微小脂肪(n=6)或 CCRCC(n=33)患者。所有患者均在接受部分肾切除术或经皮活检前接受了 MR 成像。评估肿瘤的 4 个定量参数:T1W-梯度回波成像的信号强度(SI)指数、T2 脂肪抑制成像(T2-SI 指数)的 SI 指数、表观扩散系数(ADC)值和 ADC 的标准差(SD)。采用 Wilcoxon 秩和检验和受试者工作特征(ROC)曲线分析比较这些定量参数。使用逻辑回归分析寻找最佳定量参数组合。
定量参数比较显示,小 AML 伴微小脂肪组的 T2-SI 指数(中位数,AML 伴微小脂肪与 CCRCC;0.74 比 1.27,p<0.001)、ADC 值(1.12 比 1.75,p=0.005)和 ADC 的 SD(104 比 233,p<0.001)均显著低于 CCRCC 组。ROC 曲线分析显示,使用 ADC 的 SD 和 T2-SI 指数或 ADC 值作为解释变量的逻辑回归模型的曲线下面积最高(1.000;100%灵敏度;100%特异性)。
ADC 的 SD 联合 T2-SI 指数或 ADC 值在鉴别小 AML 伴微小脂肪和 CCRCC 方面具有最高的诊断性能。