Tadayyon Hadi, Sadeghi-Naini Ali, Czarnota Gregory J
Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Transl Oncol. 2014 Dec;7(6):759-67. doi: 10.1016/j.tranon.2014.10.007.
The identification of tumor pathologic characteristics is an important part of breast cancer diagnosis, prognosis, and treatment planning but currently requires biopsy as its standard. Here, we investigated a noninvasive quantitative ultrasound method for the characterization of breast tumors in terms of their histologic grade, which can be used with clinical diagnostic ultrasound data.
Tumors of 57 locally advanced breast cancer patients were analyzed as part of this study. Seven quantitative ultrasound parameters were determined from each tumor region from the radiofrequency data, including mid-band fit, spectral slope, 0-MHz intercept, scatterer spacing, attenuation coefficient estimate, average scatterer diameter, and average acoustic concentration. Parametric maps were generated corresponding to the region of interest, from which four textural features, including contrast, energy, homogeneity, and correlation, were determined as further tumor characterization parameters. Data were examined on the basis of tumor subtypes based on histologic grade (grade I versus grade II to III).
Linear discriminant analysis of the means of the parametric maps resulted in classification accuracy of 79%. On the other hand, the linear combination of the texture features of the parametric maps resulted in classification accuracy of 82%. Finally, when both the means and textures of the parametric maps were combined, the best classification accuracy was obtained (86%).
Textural characteristics of quantitative ultrasound spectral parametric maps provided discriminant information about different types of breast tumors. The use of texture features significantly improved the results of ultrasonic tumor characterization compared to conventional mean values. Thus, this study suggests that texture-based quantitative ultrasound analysis of in vivo breast tumors can provide complementary diagnostic information about tumor histologic characteristics.
识别肿瘤病理特征是乳腺癌诊断、预后及治疗规划的重要组成部分，但目前其标准方法是活检。在此，我们研究了一种非侵入性定量超声方法，用于根据组织学分级对乳腺肿瘤进行特征描述，该方法可与临床诊断超声数据结合使用。
本研究分析了57例局部晚期乳腺癌患者的肿瘤。从射频数据中确定每个肿瘤区域的七个定量超声参数，包括中频拟合、频谱斜率、0兆赫兹截距、散射体间距、衰减系数估计值、平均散射体直径和平均声学浓度。生成对应感兴趣区域的参数图，从中确定包括对比度、能量、均匀性和相关性在内的四个纹理特征作为进一步的肿瘤特征描述参数。根据基于组织学分级的肿瘤亚型（I级与II至III级）对数据进行检查。
对参数图均值进行线性判别分析，分类准确率为79%。另一方面，参数图纹理特征的线性组合，分类准确率为82%。最后，当将参数图的均值和纹理结合起来时，获得了最佳分类准确率（86%）。
定量超声频谱参数图的纹理特征提供了关于不同类型乳腺肿瘤的判别信息。与传统均值相比，纹理特征的使用显著改善了超声肿瘤特征描述的结果。因此，本研究表明，基于纹理的体内乳腺肿瘤定量超声分析可提供关于肿瘤组织学特征的补充诊断信息。
