Mousavi Seyed Reza, Rivaz Hassan, Czarnota Gregory J, Samani Abbas, Sadeghi-Naini Ali
Department of Medical Biophysics, University of Toronto, Toronto, Ontario M4N 3M5, Canada; Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada; Department of Radiation Oncology, Odette Cancer Research, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario M4N 3M5, Canada; Department of Medical Biophysics, Western University, London, Ontario N6A 5C1, Canada.
Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec H4B 1R6, Canada; PERFORM Centre, Concordia University, Montreal, Quebec H4B 1R6, Canada.
Transl Oncol. 2017 Oct;10(5):744-751. doi: 10.1016/j.tranon.2017.06.006. Epub 2017 Jul 20.
A novel full-inversion-based technique for quantitative ultrasound elastography was investigated in a pilot clinical study on five patients for non-invasive detection and localization of prostate cancer and quantification of its extent. Conventional-frequency ultrasound images and radiofrequency (RF) data (~5 MHz) were collected during mechanical stimulation of the prostate using a transrectal ultrasound probe. Pre and post-compression RF data were used to construct the strain images. The Young's modulus (YM) images were subsequently reconstructed using the derived strain images and the stress distribution estimated iteratively using finite element (FE) analysis. Tumor regions determined based on the reconstructed YM images were compared to whole-mount histopathology images of radical prostatectomy specimens. Results indicated that tumors were significantly stiffer than the surrounding tissue, demonstrating a relative YM of 2.5±0.8 compared to normal prostate tissue. The YM images had a good agreement with the histopathology images in terms of tumor location within the prostate. On average, 76%±28% of tumor regions detected based on the proposed method were inside respective tumor areas identified in the histopathology images. Results of a linear regression analysis demonstrated a good correlation between the disease extents estimated using the reconstructed YM images and those determined from whole-mount histopathology images (r=0.71). This pilot study demonstrates that the proposed method has a good potential for detection, localization and quantification of prostate cancer. The method can potentially be used for prostate needle biopsy guidance with the aim of decreasing the number of needle biopsies. The proposed technique utilizes conventional ultrasound imaging system only while no additional hardware attachment is required for mechanical stimulation or data acquisition. Therefore, the technique may be regarded as a non-invasive, low cost and potentially widely-available clinical tool for prostate cancer diagnosis.
在一项针对五名患者的初步临床研究中，对一种基于全反转的新型定量超声弹性成像技术进行了研究，用于前列腺癌的无创检测、定位及其范围的量化。使用经直肠超声探头在对前列腺进行机械刺激期间收集传统频率超声图像和射频（RF）数据（约5兆赫兹）。压缩前后的RF数据用于构建应变图像。随后使用导出的应变图像重建杨氏模量（YM）图像，并使用有限元（FE）分析迭代估计应力分布。将基于重建的YM图像确定的肿瘤区域与前列腺根治性切除标本的全层组织病理学图像进行比较。结果表明，肿瘤比周围组织明显更硬，与正常前列腺组织相比，相对杨氏模量为2.5±0.8。YM图像在前列腺内肿瘤位置方面与组织病理学图像具有良好的一致性。平均而言，基于所提出方法检测到的肿瘤区域中有76%±28%位于组织病理学图像中确定的相应肿瘤区域内。线性回归分析结果表明，使用重建的YM图像估计的疾病范围与从全层组织病理学图像确定的疾病范围之间具有良好的相关性（r = 0.71）。这项初步研究表明，所提出的方法在前列腺癌的检测、定位和量化方面具有良好的潜力。该方法有可能用于前列腺穿刺活检引导，以减少穿刺活检的次数。所提出的技术仅利用传统超声成像系统，在机械刺激或数据采集时无需额外的硬件附件。因此，该技术可被视为一种用于前列腺癌诊断的无创、低成本且可能广泛可用的临床工具。
