乳腺专用γ显像的图像特征对乳腺病变的鉴别诊断价值

Efficacy of the image features of breast-specific Gamma imaging in the differential diagnosis of breast lesions

  • 摘要:
    目的 探讨乳腺专用γ显像(BSGI)的图像特征对乳腺病变的鉴别诊断价值。
    方法 回顾性分析2014年7月至2015年6月行BSGI的272例中国女性乳腺疾病患者(共293个病灶),观察BSGI图像上病灶形态、病灶边缘是否清晰、灰度分布是否存在“偏心核心”、乳腺影像报告和数据系统(BIRADS)结果以及最大肿瘤/非肿瘤比值(T/NT)。采用二变量秩相关分析及二分类Logistic回归分析法计算上述特征与病理结果的相关性。基于病灶计算所有图像特征的独立诊断效能以及上述显著相关特征的合并诊断效能,用MedCalc软件行Z检验比较上述特征的受试者工作特征曲线。
    结果 病灶形态(OR=0.013,95%CI:3.664~21.846,P=0.000)、病灶边缘是否清晰(OR=2.121,95%CI:1.061~4.239,P=0.033)以及灰度分布是否存在“偏心核心”(OR=12.927,95%CI:5.415~30.863,P=0.000)与病理结果显著相关。三者的灵敏度、特异度分别为92.0%(172/187)和58.5%(62/106)、66.8%(125/187)和71.7%(76/106)、95.7%(179/187)和27.4%(29/106)。三者合并诊断效能最佳,灵敏度、特异度、阳性预测值、阴性预测值、准确率分别为88.2%(165/187)、81.1%(86/106)、89.2%(165/185)、79.6%(86/108)和85.7%(251/293),较BIRADS以及最大T/NT(界值:1.75)更准确,且差异均有统计学意义(Z=4.079、4.090,均P<0.05)。
    结论 病灶形态、病灶边缘是否清晰以及灰度分布是否存在“偏心核心”可作为BSGI鉴别诊断乳腺病灶的图像特征,三者联合诊断能提高BSGI在乳腺病变中的独立诊断价值。

     

    Abstract:
    Objective To investigate the image features of breast specific gamma imaging(BSGI) in the differential diagnosis of breast lesions.
    Methods A total of 272 Chinese female patients(including 293 lesions) who underwent BSGI between July 2014 to June 2015 were included. Several characteristics of the shape of the lesion, clarity of the boundary, grey scale distribution(the existence of a decentered core), breast imaging reporting and data system(BIRADS), and maximum tumor to non-tumor ratio(T/NT) were recorded. The correlation of each feature with the pathology was evaluated by rank correlation analysis and binary logistic regression analysis. All features were used in the diagnosis of the 293 lesions. Each independent lesion-based diagnostic performance as well as the combined diagnostic efficacy of statistically significant features were evaluated. Using MedCalc software, Z test based on receiver operating characteristic curve was used between each pair of image features to figure out their possible differences.
    Results Three imaging features including the shape of the lesion(OR=0.013, 95%CI: 3.664–21.846, P=0.000), the clarity of boundary(OR=2.121, 95%CI: 1.061–4.239, P=0.033), and grey scale distribution(the existence of a decentered core)(OR=12.927, 95%CI: 5.415–30.863, P=0.000) were significantly related with the pathology. The sensitivity and specificity of the three former characteristics were 92.0%(172/187) and 58.5%(62/106), 66.8%(125/187) and 71.7%(76/106), and 95.7%(179/187) and 27.4%(29/106), respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the combined diagnosis of the three former image characteristics were 88.2%(165/187), 81.1%(86/106), 89.2%(165/185), 79.6%(86/108), and 85.7%(251/293), respectively. With the best performance of all, this combined diagnosis has a higher diagnostic performance than BIRADS and maximum T/NT(cutoff ratio:1.75)(Z=4.079 and 4.090, both P<0.05).
    Conclusions The shape of the lesion, the clarity of boundary, and the grey-scale distribution(the existence of a decentered core) could be three important differential diagnostic standards of breast lesions in BSGI. With the combined diagnosis of the three features, the efficacy of independent diagnosis of BSGI in breast lesions could be improved.

     

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