动态增强MRI半定量信号强度-时间曲线及全定量灌注参数在子宫肿瘤中的应用

目的：探讨3.0T动态增强MRI半定量信号强度-时间(signal intensity-time，SI-Time)曲线及全定量灌注参数在子宫病变中的应用价值。方法：回顾性分析经病理证实为子宫恶性肿瘤20例，良性肿瘤或肿瘤样病变22例。均行3.0T动态增强MRI扫描，并使用Siemens Tissue 4D软件进行图像后处理，勾画感兴趣区并绘制SI-Time曲线。进一步测得感兴趣区的容积定量灌注参数值：容量转移常数(Ktrans)、速率常数(Kep)、血管外细胞外间隙容积比(Ve)。对良、恶性病变组的SI-Time曲线类型进行统计学分析，对良、恶性病变组的定量灌注参数值进行单因素方差分析，多个样本均数的两两比较。结果：20例恶性肿瘤中宫颈癌12例，内膜癌8例；22例良性病变中13例为平滑肌瘤，3例为子宫内膜息肉，3例内膜增生，3例为子宫腺肌症。良性组以I型曲线(59.1%)为主；恶性组以II型曲线(65.0%)为主。不同类型的SI-Time曲线在良恶性病变之间差异具有统计学意义(P=0.011)。若以I型曲线作为诊断子宫良性病变的标准，II和III型曲线作为诊断子宫恶性病变的标准，则诊断灵敏度为90%，特异性为59.1%，阳性预测值为66.7%，阴性预测值为86.7%。恶性病变组Ve值比良性病变组[(0.477±0.143) vs (0.589±0.176)，P=0.004]和对照组[(0.477±0.143) vs (0.614±0.146)，P=0.004]均低；恶性病变组Ktrans值比对照组低[(0.178±0.067)min−1 vs (0.263±0.111)min−1，P=0.003]；良性病变组Ktrans比对照组低[(0.182±0.096)min−1 vs (0.263±0.111)min−1，P=0.011]。结论：动态增强MRI半定量SI-Time曲线及全定量灌注参数值在子宫良恶性病变鉴别诊断中具有积极意义，可作为常规MRI形态学诊断的有效补充手段。

Signal intensity-time curve and quantitative dynamic contrast-enhanced magnetic resonance imaging in differentiating neoplasms of uterus

Objective: To evaluate signal intensity-time (SI-Time) curve and quantitative dynamic contrast-enhanced 3.0T magnetic resonance imaging in diagnosis and differentiating neoplasm of uterus.Methods: A total of 42 cases of uterine neoplasm (20 were malignant and 22 were benign) were evaluated in our study. All cases received dynamic contrast-enhanced scanning on 3.0T MRI. The raw data was processed by Siemens Tissue 4D software and the SI-Time curve was obtained and analyzed. Pharmacokinetic modeling of Tofts with a modeled vascular input function was used for calculating volume parameters: volume transfer constant (Ktrans), reverse volume transfer constant (Kep), the extravascular extracellular space volume per unit volume of tissue (Ve). The correlation of these parameters at each groups were investigated. The SI-Time curve and the data of perfusion parameters between the 2 groups were compared by T test.Results: Among 20 malignant tumors, 12 were cervical carcinoma and 8 were endometrial cancer. Among the benign tumors, 13 were leiomyomas, 3 were endometrial polyp, 3 were endometrial hyperplasia, and 3 were adenomyosis. 59.1% cases of benign tumors belong to Type I curve and 65% cases of malignant tumors belong to Type II curve. There was significant difference in SI-Time curve between benign and malignant tumors (P=0.011). If Type I curve was used as diagnostic criteria for benign tumors, and Type II and III curve were for malignant tumors, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value were 90.0%, 59.1%, 66.7%, and 86.7%, respectively. Ve was 0.477±0.143 in malignant and 0.614±0.146 in control group with significant difference (P=0.004). Ve was 0.477±0.143 in malignant and 0.589±0.176 in benign group with significant difference (P=0.004). Ktrans was (0.178±0.067) min−1 in malignant and (0.263±0.111)min−1 in control group with significant difference (P=0.003). Ktrans was (0.182±0.096) min−1 in benign and (0.263±0.111) min−1 in control group with significant difference (P=0.011). Conclusion: The type of SI-Time curve and perfusion parameters were important for differentiating benign and malignant uterine tumors in dynamic enhanced MRI. These parameters provide a supplement for conventional morphological MR diagnosis.