摘要
目的探讨超声造影对卵巢肿瘤的诊断与鉴别诊断价值。方法用常规超声筛选出符合本研究对象的卵巢肿瘤病例,向患者说明超声造影的意义,争取病人的知情合作。采用造影剂声诺维(Sonovue),并用随机配置的软件绘制时间-强度曲线进行分析,观察卵巢良恶性肿瘤形态及灌注特征,并与常规二维超声及手术病理结果对照。结果卵巢良性肿瘤绝大多数显示瘤体均匀性增强,多数从周边向内部灌注,可见血管形态规则;而恶性肿瘤显示瘤体不均匀性增强,多数以血管为中心向瘤体内部灌注,可见粗大、扭曲、走向不规则的供养血管呈蟹足样或树枝状穿入瘤内,而且恶性肿瘤较良性肿瘤血流灌注早,峰值基础高,峰值前移;而转移性肿瘤,峰值基础高。结论超声造影联合随机配置的软件绘制时间-强度曲线可以实时动态地反映卵巢肿瘤的血流灌注情况,对时间-信号强度曲线各项参数的分析可量化病灶的灌注特征,有助于卵巢肿瘤的诊断与鉴别诊断。
Objective To vestigate the value in diagnosis and differential diagnosis from the contrast-enhanced ultrasound of ovarian tumors.Methods Screen the cases of the ovarian tumor study with the conventional ultrasound,explain the significance of the contrast-enhanced ultrasound to the patients,and manage to get their informed cooperation,use imaging agent SonoVue and the software configured to draw time-intensity curve and analyse, observe the morphology and perfusion characteristics of benign and malignant ovarian tumors, contrast the result from the conventional two-dimensional ultrasound and surgical pathological results.Results Most of the benign ovarian tumor showed homogeneous enhancement,the majority from the peripheral to the internal perfusion,that vascular patterns can be seen rules; and malignant tumor showed inhomogeneous enhancement the majority as the center of ressds goes into the tumor, showing large, distorted,and that irregular keloid was dependent vascular or dendritic-like tumor penetration.Malignant tumor perfusions early,than beginning tumor based on a high peak, peak forward; and metastatic tumors,based on a high peak. Conclusion: Ultrasound imaging of joint random configuration soft ware rendering time-intensity curve can reflect the real-time dynamic blood flow in ovarian tumors,the time-signal intensity curve analysis of the parameters can be quantified perfusion lesion characteristics,will help ovarian tumors in the diagnosis and differential diagnosis.
出处
《中外医疗》
2010年第13期16-17,共2页
China & Foreign Medical Treatment
基金
<卵巢肿瘤的超声造影及临床诊断研究>湛江市科技局(编号:2006C06020)
关键词
卵巢肿瘤
超声造影
诊断
Ovarian tumors
contrast-enhanced sonography
Diagnosis