MRI与多层螺旋CT检查在巨大浆膜下子宫肌瘤中的诊断价值
The diagnostic value of MRI and multi-slice spiral CT in the diagnosis of large subserous uterine fibroids
曹迎娣 1张凌1
作者信息
- 1. 南京市高淳人民医院放射科,江苏南京 211300
- 折叠
摘要
目的 探究磁共振成像(MRI)与多层螺旋CT在巨大浆膜下子宫肌瘤患者中的诊断价值.方法 选取55例疑似巨大浆膜下子宫肌瘤患者,均采用MRI、多层螺旋CT诊断,以手术病理诊断结果为金标准,分析MRI、多层螺旋CT的诊断结果以及比较MRI、多层螺旋CT的阳性检出情况、诊断效能.结果 经手术病理诊断,55例疑似巨大浆膜下子宫肌瘤患者中,确诊巨大子宫肌瘤35例(63.64％);MRI诊断巨大浆膜下子宫肌瘤30例,检出率为85.71％;多层螺旋CT诊断巨浆膜下大子宫肌瘤27例,检出率为77.14％.MRI与多层螺旋CT阳性检出率比较,差异无统计学意义(P＞0.05).MRI与多层螺旋CT敏感度、特异度、准确率比较差异无统计学意义(P＞0.05).结论 在巨大浆膜下子宫肌瘤疾病诊断中,MRI、多层螺旋CT均可达到预期的诊断效果,临床诊断中可将两种诊断方法联合应用,能够达到更高的诊断效能,值得临床推广.
Abstract
Objective To explore the diagnostic value of magnetic resonance imaging(MRI)and multi-slice spiral CT in patients with large subserous uterine fibroids.Methods 55 patients with suspected giant subserous uterine fibroids were selected and diagnosed by MRI and multi-slice spiral CT.The diagnostic results of MRI and multi-slice spiral CT were analyzed,and the positive detection and diagnostic efficiency of MRI and multi-slice spiral CT were compared.Results After surgical and pathological diagnosis,35 cases(63.64％)of 55 suspected patients with giant subserous fibroids were confirmed.MRI diagnosis of giant subserons uterine fibroids in 30 cases,the detection rate was 85.71％;Multislice spiral CT diagnosed 27 cases of giant subserons uterine fibroids,the detection rate was 77.14％.There was no significant difference in the positive detection rate between MRI and multi-slice spiral CT(P＞0.05).There was no significant difference in sensitivity,specificity and accuracy between MRI and multi-slice spiral CT(P＞0.05).Conclusion In the diagnosis of giant subserous uterine fibroids,both MRI and multi-slice spiral CT can achieve the expected diagnostic effect,and the two diagnostic methods can be combined in clinical diagnosis,which can achieve higher diagnostic efficiency and is worthy of clinical promotion.
关键词
巨大浆膜下子宫肌瘤/磁共振成像/多层螺旋CTKey words
Giant subserous uterine myoma/Magnetic resonance imaging/Multislice spiral CT引用本文复制引用
出版年
2024
NETL
NSTL
万方数据