| Logistic回归模型评价自动乳腺全容积扫描联合常规超声诊断乳腺影像报告及数据系统3~5类结节 |
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| 引用本文： | 李静敏,邵玉红,孙秀明,张惠,李鹏,王彬.Logistic回归模型评价自动乳腺全容积扫描联合常规超声诊断乳腺影像报告及数据系统3~5类结节[J].中国介入影像与治疗学,2018,15(5):282-285. |
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| 作者姓名： | 李静敏 邵玉红 孙秀明 张惠 李鹏 王彬 |
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| 作者单位： | 北京大学第一医院超声科 |
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| 摘 要： | 目的探讨多因素Logistic回归分析评价自动乳腺全容积扫描(ABVS)联合常规超声鉴别诊断乳腺影像报告及数据系统(BI-RADS)3～5类结节良恶性的价值。方法 216例患者(247个BI-RADS 3～5类乳腺结节)接受常规超声及ABVS检查。以穿刺活检或术后病理为金标准,建立多因素Logistic回归模型,筛选出诊断乳腺恶性肿瘤的主要超声特征,并评价其鉴别诊断乳腺BI-RADS 3～5类结节的价值。绘制ROC曲线,评价Logistic回归模型的诊断效能。结果多因素Logistic回归筛选出4个诊断BI-RADS 3～5类结节良恶性的独立危险因素,为"皱缩征"X_1,优势比(OR)=12.03,P0.01)、"微分叶征"(X_2,OR=6.00,P0.01)、结节边界(X_6,OR=11.48,P=0.01)和纵横比(X_8,OR=4.09,P=0.01),其中"皱缩征"的OR最大。回归方程为Logit(P)=-4.43+2.49 X_1+1.79 X_2+2.44 X_6+1.41 X_8(χ~2=196.32,P0.01)。Logistic回归模型预测乳腺恶性结节的ROC曲线下面积为0.95(P0.01)。结论 Logistic回归模型鉴别诊断BI-RADS 3～5类良恶性结节有较高价值。
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| 关 键 词： | 超声检查 乳腺肿瘤 Logistic回归 |
| 收稿时间： | 2017/10/17 0:00:00 |
| 修稿时间： | 2018/3/18 0:00:00 |
Logistic regression model in evaluation on diagnosis of breast nodules classification of breast imaging reporting and data system 3-5 category with automated breast volume scanner combined with conventional ultrasound |
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| LI Jingmin,SHAO Yuhong,SUN Xiuming,ZHANG Hui,LI Peng and WANG Bin.Logistic regression model in evaluation on diagnosis of breast nodules classification of breast imaging reporting and data system 3-5 category with automated breast volume scanner combined with conventional ultrasound[J].Chinese Journal of Interventional Imaging and Therapy,2018,15(5):282-285. |
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| Authors: | LI Jingmin SHAO Yuhong SUN Xiuming ZHANG Hui LI Peng and WANG Bin |
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| Institution: | Department of Ultrasound, Peking University First Hospital, Beijing 100034, China,Department of Ultrasound, Peking University First Hospital, Beijing 100034, China,Department of Ultrasound, Peking University First Hospital, Beijing 100034, China,Department of Ultrasound, Peking University First Hospital, Beijing 100034, China,Department of Ultrasound, Peking University First Hospital, Beijing 100034, China and Department of Ultrasound, Peking University First Hospital, Beijing 100034, China |
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| Abstract: | |
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| Keywords: | Ultrasonography Breast neoplasms Logistic regression |
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