Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.
Ultrasound Med Biol. 2020 May;46(5):1142-1157. doi: 10.1016/j.ultrasmedbio.2020.01.022. Epub 2020 Feb 25.
Quantitative ultrasound (QUS) techniques have been demonstrated to detect cell death in vitro and in vivo. Recently, multi-feature classification models have been incorporated into QUS texture-feature analysis methods to increase further the sensitivity and specificity of detecting treatment response in locally advanced breast cancer patients. To effectively incorporate these analytic methods into clinical applications, QUS and texture-feature estimations should be independent of data acquisition systems. The study here investigated the consistencies of QUS and texture-feature estimation techniques relative to several factors. These included the ultrasound system properties, the effects of tissue heterogeneity and the effects of these factors on the monitoring of response to neoadjuvant chemotherapy. Specifically, tumour-response-detection performance based on QUS and texture parameters using two clinical ultrasound systems was compared. Observed variations in data between the systems were small and the results exhibited good agreement in tumour response predictions obtained from both ultrasound systems. The results obtained in this study suggest that tissue heterogeneity was a dominant feature in the parameters measured with the two different ultrasound systems; whereas differences in ultrasound system beam properties only exhibited a minor impact on texture features. The McNemar statistical test performed on tumour response prediction results from the two systems did not reveal significant differences. Overall, the results in this study demonstrate the potential to achieve reliable and consistent QUS and texture-based analyses across different ultrasound imaging platforms.
定量超声（QUS）技术已被证明可用于体外和体内检测细胞死亡。最近，多特征分类模型已被纳入 QUS 纹理特征分析方法中，以进一步提高检测局部晚期乳腺癌患者治疗反应的灵敏度和特异性。为了有效地将这些分析方法应用于临床，QUS 和纹理特征估计应独立于数据采集系统。本研究调查了 QUS 和纹理特征估计技术相对于几个因素的一致性。这些因素包括超声系统特性、组织异质性的影响以及这些因素对新辅助化疗反应监测的影响。具体来说，比较了两种临床超声系统的 QUS 和纹理参数的肿瘤反应检测性能。两个系统之间的数据变化很小，两个超声系统获得的肿瘤反应预测结果具有很好的一致性。本研究结果表明，组织异质性是两种不同超声系统测量的参数的主要特征；而超声系统束特性的差异仅对纹理特征有较小的影响。对两个系统的肿瘤反应预测结果进行的 McNemar 统计检验没有显示出显著差异。总体而言，本研究的结果表明，在不同的超声成像平台上实现可靠和一致的 QUS 和基于纹理的分析是有可能的。
