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机构地区:[1]南京明基医院 [2]南京医科大学附属明基医院放射科,江苏南京210019 [3]北京大学第一医院医学影像科,北京100034
出 处:《中国医学影像技术》2011年第9期1850-1854,共5页Chinese Journal of Medical Imaging Technology
摘 要:目的对比观察CT仿真结肠镜(CTC)与腹部增强CT对炎症性肠病(IBD)的诊断价值,探讨IBD的影像学表现。方法收集IBD患者36例及非IBD患者36例,IBD患者中17例接受增强CT检查,另19例接受CTC检查;非IBD患者中接受增强CT及CTC检查者各18例。对图像进行盲法阅片,对IBD检出的可信度分5级进行评价,采用ROC曲线进行分析,并以Logistic回归统计方法筛选能预测IBD的相关影像学征象。结果 ROC曲线显示CTC的曲线下面积(Az)为0.9354,明显高于增强CT(Az=0.6841,P=0.009)。多因素Logistic回归分析表明3项影像学改变可预测IBD,包括:①结肠黏膜不光滑、呈颗粒状;②肠壁增厚及强化;③结肠袋消失。以肠壁增厚及强化诊断IBD的敏感度最高,达80.62%;结肠黏膜不光滑、呈颗粒状+结肠袋消失相结合的特异度最高,达97.20%。结论与腹部增强CT相比,CTC对IBD具有更高的检出率。结肠肠壁增厚伴强化是诊断IBD最具敏感性的征象,IBD最特异性的征象是同时具备结肠黏膜不光滑、呈颗粒状和结肠袋消失。Objective To observe the clinical value of CT virtual colonoscopy(CTC) compared with enhanced abdominal CT in diagnosis of suspected inflammatory bowel diseases(IBD),and to explore the imaging features of IBD.Methods Thirty-six patients with IBD(17 underwent enhanced CT,19 underwent CTC) and 36 patients without IBD(18 underwent enhanced CT and 18 underwent CTC) were enrolled.All images were interpreted by two independent observers using a five-point scale for the detection of lesions.ROC curves for enhanced abdominal CT and CTC were analyzed.Logistic regression analysis was used to reveal imaging features to predict IBD.Results ROC analysis indicated that Az of CTC(0.9354) was significantly larger(P=0.009) than that of enhanced CT(0.6841).Three imaging features were demonstrated as significant predictive factors of IBD by multiple factor Logistic regression analysis,including: ①Granular appearance of colonic mucosa;②Bowel wall thickness and enhancement;③Loss of haustra coli.Using the sign of bowel wall thickness and enhancement alone to predict IBD,the sensitivity was the highest(80.62%),while combining the other two signs,the specificity was the highest(97.20%).Conclusion CTC was superior to enhanced CT in diagnosis of IBD.Bowel wall thickness and enhancement is the most sensitive sign in predicting IBD,and combination of granular appearance of colonic mucosa and loss of haustra coli has the highest specificity.
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