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光谱CT平扫定量参数鉴别诊断乳腺良、恶性病变 被引量:1

Plain spectral CT quantitative parameters for differential diagnosis of benign and malignant breast lesions
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摘要 目的观察光谱CT平扫定量参数鉴别诊断乳腺良、恶性病变的价值。方法回顾性分析110例女性乳腺占位性病变患者的胸部光谱CT资料,包括53例共53处恶性病变(恶性组)和57例共60处良性病变(良性组);对比组间光谱CT平扫定量参数的差异,包括虚拟单能级40~100 keV下CT值、有效原子序数(Eff-Z)及光谱曲线斜率(λHU)。绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC),分析光谱CT平扫单一定量参数CT_(40 keV)、CT_(70 keV)、Eff-Z、λHU及其联合鉴别诊断乳腺良、恶性病变的效能。结果40~100 keV下,恶性组CT值及Eff-Z均高于、而λHU小于良性组(P均<0.001)。单一CT_(40 keV)、CT_(70 keV)、Eff-Z及λHU鉴别乳腺良、恶性病变的AUC分别为0.90、0.80、0.87及0.87;四者联合的AUC为0.91,其敏感度为84.9%,特异度为88.3%,准确率为85.0%。结论根据光谱CT平扫定量参数可有效鉴别诊断乳腺良、恶性病变。 Objective To observe the value of quantitative parameters of plain spectral CT for differential diagnosis of benign and malignant breast lesions.Methods Data of chest spectral CT of 110 female patients with space occupying breast lesions were retrospectively analyzed,including 53 malignant lesions in 53 cases(malignant group)and 60 benign lesions in 57 cases(benign group).Spectral CT plain quantitative parameters,including CT value of virtual single energy level of 40—100 keV,effective atomic number(Eff-Z)and spectral curve slope(λHU)were compared between the two groups.Receiver operating characteristic(ROC)curves were drawn,and the areas under the curve(AUC)were calculated to evaluate the efficacy of spectral CT plain quantitative parameters CT_(40 keV),CT_(70 keV),Eff-Z andλHU alone and in combination for differential diagnosis of benign and malignant breast lesions.Results Under 40—100 keV,in malignant group,CT values and Eff-Z were higher butλHU was lower than those in benign group(all P<0.001).AUC of CT_(40 keV),CT_(70 keV),Eff-Z andλHU for differentiating benign and malignant breast lesions alone was 0.90,0.80,0.87 and 0.87,respectively,of the combination of the above four was 0.91,with the sensitivity of 84.9%,the specificity of 88.3%and the accuracy of 85.0%.Conclusion Plain spectral CT quantitative parameters could be used to effectively differentiate benign and malignant breast lesions.
作者 刘思腾 于湛 李婧琳 王洁洁 杨芸晓 LIU Siteng;YU Zhan;LI Jinglin;WANG Jiejie;YANG Yunxiao(Department of Radiology,the First Affiliated Hospital of Zhengzhou University,Zhengzhou 450052,China)
出处 《中国医学影像技术》 CSCD 北大核心 2023年第8期1196-1200,共5页 Chinese Journal of Medical Imaging Technology
基金 国家重点研发计划项目(Y2017YFC112602)。
关键词 乳腺肿瘤 诊断 鉴别 体层摄影术 X线计算机 breast neoplasms diagnosis,differential tomography,X-ray computed
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