Leng Xiaoling, Huang Guofu, Yao Lanhui, Ma Fucheng
Department of Ultrasonography, The First Affiliated Hospital of Xinjiang Medical University Urumqi 830054, Xinjiang, China ; Department of Ultrasonography, The Affiliated Tumor Hospital of Xinjiang Medical University Urumqi 830011, Xinjiang, China.
Department of Radio-Chemotherapy, The Fifth Affiliated Hospital of Xinjiang Medical University Urumqi 830011, Xinjiang, China.
Int J Clin Exp Med. 2015 Sep 15;8(9):15889-99. eCollection 2015.
This study is to investigate the diagnostic role of multi-mode ultrasound in level 4 BI-RADS breast lesions and to establish a Logistic regression model.
Totally 179 patients with 182 sites of breast lesions were enrolled in this study. Preoperatively, the examinations of routine ultrasonography, elastography, contrast-enhanced ultrasonography and three-dimensional color Doppler were performed. Postoperatively, the breast lesions were diagnosed as benign and malignant lesions according to pathological results. Diagnostic indicators of each ultrasound analysis were determined and compared. The relationship between these diagnostic indicators and the benign and malignant features of breast lesions was analyzed by single factor analysis. Logistic regression model was established.
The diagnostic indicators with high sensitivity and specificity were tumor edge, enhanced range and score of elastography. Four factors of tumor edge, enhanced order, contrast mode and score of elastography were related with the benign and malignant features of breast lesions. The prediction model was Logit (P) = 0.636 + 4.471X1 + 4.337X2 + 3.753X3 + 3.014X4 + 2.525X5 + 2.105X6. Likelihood ratio test showed that the model was statistically significant (χ(2) = 161.876, P < 0.0001). This model could effectively distinguish between benign and malignant tumors (R(2) = 0.813, prediction accuracy 92.3%). The differences in sensitivity and specificity between multi-mode ultrasound diagnosis and routine ultrasound diagnosis were statistically significant (P < 0.001). However, there was no significant difference between Logistic regression model and multi-mode ultrasound diagnosis.
Multi-mode ultrasound and Logistic regression model are more effective in diagnosing level 4 BI-RADS breast lesions.
本研究旨在探讨多模态超声在4类BI-RADS乳腺病变中的诊断作用,并建立Logistic回归模型。
本研究共纳入179例患有182处乳腺病变的患者。术前进行常规超声、弹性成像、超声造影及三维彩色多普勒检查。术后根据病理结果将乳腺病变诊断为良性和恶性病变。确定并比较各超声分析的诊断指标。通过单因素分析分析这些诊断指标与乳腺病变良恶性特征之间的关系。建立Logistic回归模型。
敏感性和特异性较高的诊断指标为肿瘤边缘、增强范围及弹性成像评分。肿瘤边缘、增强顺序、造影模式及弹性成像评分这四个因素与乳腺病变的良恶性特征相关。预测模型为Logit(P)=0.636 + 4.471X1 + 4.337X2 + 3.753X3 + 3.014X4 + 2.525X5 + 2.105X6。似然比检验显示该模型具有统计学意义(χ(2)=161.876,P<0.0001)。该模型能有效区分良性和恶性肿瘤(R(2)=0.813,预测准确率92.3%)。多模态超声诊断与常规超声诊断在敏感性和特异性上的差异具有统计学意义(P<0.001)。然而,Logistic回归模型与多模态超声诊断之间无显著差异。
多模态超声和Logistic回归模型在诊断4类BI-RADS乳腺病变方面更有效。