Mai Hui, Mao Yifei, Dong Tianfa, Tan Yu, Huang Xiaowei, Wu Songxin, Huang Shuting, Zhong Xi, Qiu Yingwei, Luo Liangping, Jiang Kuiming
Department of Medical Imaging, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Front Oncol. 2019 Oct 15;9:1021. doi: 10.3389/fonc.2019.01021. eCollection 2019.
The preoperative diagnosis of phyllodes tumors (PTs) of the breast is critical to appropriate surgical treatment. However, reliable differentiation between PT and fibroadenoma (FA) remains difficult in daily clinical practice. The purpose of this study was to investigate the utility of breast MRI texture analysis for differentiating PTs from FAs. Forty-two PTs and 42 FAs were enrolled in this retrospective study. Clinical and conventional MRI features (CCMF) and MRI texture analysis were used to distinguish between PT and FA. Texture features were extracted from the axial short TI inversion recovery T2-weighted (T2W-STIR), T1-weighted pre-contrast, and two contrast-enhanced series (first contrast and third contrast). The Mann-Whitney test was used to select statistically significant features of texture analysis and CCMF. Using a linear discriminant analysis, the most discriminative features were determined from statistically significant features. The K-nearest neighbor classifier and ROC curve were applied to evaluate the diagnostic performance. With a higher classification accuracy (89.3%) and an AUC of 0.89, the texture features on T2W-STIR outperformed the texture features on other MRI sequences and CCMF. The AUC of the combination of CCMF with texture features on T2W-STIR was significantly higher than that of CCMF or texture features on T2W-STIR alone ( < 0.05). Based on the result of the classification accuracy (95.2%) and AUC (0.95), the diagnostic performance of the combination strategy performed better than texture features on T2W-STIR or CCMF separately. Texture features on T2W-STIR showed better diagnostic performance compared to CCMF for the distinction between PTs and FAs. After further validation of multi-institutional large datasets, MRI-based texture features may become a potential biomarker and be a useful medical decision tool in clinical trials having patients with breast fibroepithelial neoplasms.
乳腺叶状肿瘤(PTs)的术前诊断对于恰当的手术治疗至关重要。然而,在日常临床实践中,PT与纤维腺瘤(FA)之间的可靠鉴别仍然困难。本研究的目的是探讨乳腺MRI纹理分析在鉴别PTs与FAs中的应用价值。本回顾性研究纳入了42例PTs和42例FAs。采用临床及传统MRI特征(CCMF)和MRI纹理分析来区分PT和FA。从轴位短TI反转恢复T2加权(T2W-STIR)、T1加权预增强以及两个对比增强序列(首次对比和第三次对比)中提取纹理特征。采用曼-惠特尼检验来选择纹理分析和CCMF的统计学显著特征。通过线性判别分析,从统计学显著特征中确定最具鉴别力的特征。应用K近邻分类器和ROC曲线来评估诊断性能。T2W-STIR上的纹理特征具有更高的分类准确率(89.3%)和0.89的AUC,优于其他MRI序列上的纹理特征和CCMF。CCMF与T2W-STIR上的纹理特征相结合的AUC显著高于单独的CCMF或T2W-STIR上的纹理特征(<0.05)。基于分类准确率(95,2%)和AUC(0.95)的结果,联合策略的诊断性能优于单独的T2W-STIR上的纹理特征或CCMF。与CCMF相比,T2W-STIR上的纹理特征在区分PTs和FAs方面显示出更好的诊断性能。在多机构大样本数据集进一步验证后,基于MRI的纹理特征可能成为一种潜在的生物标志物,并在有乳腺纤维上皮性肿瘤患者的临床试验中成为一种有用的医学决策工具。