Ma Wenjuan, Guo Xinpeng, Liu Liangsheng, Qi Lisha, Liu Peifang, Zhu Ying, Jian Xiqi, Xu Guijun, Wang Xin, Lu Hong, Zhang Chao
Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer Huanhuxi Road, Hexi District, Tianjin 300060, P. R. China.
Key Laboratory of Breast Cancer Prevention and Therapy Huanhuxi Road, Hexi District, Tianjin 300060, P. R. China.
Am J Transl Res. 2020 May 15;12(5):2083-2092. eCollection 2020.
This study aimed to differentiate benign and non-benign (borderline/malignant) phyllodes tumors of the breast by the semantic and quantitative features in magnetic resonance imaging (MRI).
The female patients, diagnosed with phyllodes tumors by MRI and pathological test, were retrospectively selected from December, 2006 to April, 2019. The MRI of benign, borderline and malignant phyllodes tumors was analyzed using 8 semantic features and 20 computed quantitative features from diffuse contrast-enhanced magnetic resonance imaging (DCE-MRI). The semantic features were analyzed by univariate analysis. The least absolute shrinkage and selection operator (LASSO) method was used to identify the optimal subset of MRI quantitative features. According to the results from multivariate logistic regression for the semantic and quantitative features, the model was constructed to differentiate benign and non-benign (borderline/malignant) phyllodes tumors.
Thirty-two benign (58.18%), 13 borderline (23.64%) and 10 malignant (18.18%) phyllodes tumors were identified in 54 patients. Five semantic features were proved to be significantly correlated with pathologic grade, including size, the T1 weighted image signal intensity, fat-saturated T2-weighted image signal intensity, enhanced signal intensity, and kinetic curve pattern. With the analysis of LASSO method, three quantitative texture features with significant predictive ability were selected. The model combining both the semantic and quantitative features was proved to have good performance in differentiation on phyllodes tumors, yielding an area under receiver operating characteristic curve, accuracy, sensitivity and specificity of 0.893, 0.933, 1.000, and 0.818, respectively.
The constructed model based on the semantic and quantitative features of DCE-MRI can significantly improve the differential diagnosis of phyllodes tumors in breast.
本研究旨在通过磁共振成像（MRI）的语义和定量特征来区分乳腺的良性和非良性（交界性/恶性）叶状肿瘤。
回顾性选取2006年12月至2019年4月间经MRI和病理检查确诊为叶状肿瘤的女性患者。使用来自弥散对比增强磁共振成像（DCE-MRI）的8个语义特征和20个计算出的定量特征，对良性、交界性和恶性叶状肿瘤的MRI进行分析。通过单因素分析对语义特征进行分析。采用最小绝对收缩和选择算子（LASSO）方法来确定MRI定量特征的最佳子集。根据语义和定量特征的多变量逻辑回归结果，构建区分良性和非良性（交界性/恶性）叶状肿瘤的模型。
54例患者中，确诊为32例良性（58.18%）、13例交界性（23.64%）和10例恶性（18.18%）叶状肿瘤。5个语义特征被证明与病理分级显著相关，包括大小、T1加权图像信号强度、脂肪饱和T2加权图像信号强度、增强信号强度和动力学曲线模式。通过LASSO方法分析，选择了3个具有显著预测能力的定量纹理特征。结合语义和定量特征的模型在叶状肿瘤的鉴别诊断中表现良好，受试者工作特征曲线下面积、准确率、敏感性和特异性分别为0.893、0.933、1.000和0.818。
基于DCE-MRI语义和定量特征构建的模型可显著提高乳腺叶状肿瘤的鉴别诊断能力。
