Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China.
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Eur Radiol. 2022 Sep;32(9):5843-5851. doi: 10.1007/s00330-022-08662-1. Epub 2022 Mar 22.
To systematically assess the reproducibility of radiomics features from ultrasound (US) images during image acquisition and processing.
A standardized phantom was scanned to obtain US images. Reproducibility of radiomics features from US images, also known as ultrasomics features, was explored via (a) intra-US machine: changing the US acquisition parameters including gain, focus, and frequency; (b) inter-US machine: comparing three different scanners; (c) changing segmentation locations; and (d) inter-platform: comparing features extracted by the Ultrasomics and PyRadiomics algorithm platforms. Reproducible ultrasomics features were selected based on coefficients of variation.
A total of 108 US images from three scanners were obtained; 5253 ultrasomics features including seven categories of features were extracted and evaluated for each US image. From intra-US machine analysis, 37.0-38.8% of features showed good reproducibility. From inter-US machine analysis, 42.8% (2248/5253) of features exhibited good reproducibility. From segmentation location analysis, 55.7-57.6% of features showed good reproducibility. No significant difference in the normalized feature ranges was found between the 100 features extracted by the Ultrasomics and PyRadiomics platforms with the same algorithm (p = 0.563). A total of 1452 (27.6%) ultrasomics features were reproducible whenever intra-/inter-US machine or segmentation location were changed, most of which were wavelet and shearlet features.
Different acquisition parameters, US scanners, segmentation locations, and feature extraction platforms affected the reproducibility of ultrasomics features. Wavelet and shearlet features showed the best reproducibility across all procedures.
• Different acquisition parameters, US scanners, segmentation locations, and feature extraction platforms affected the reproducibility of ultrasomics features. • A total of 1452 (27.6%) ultrasomics features were reproducible whenever intra-/inter-US machine or segmentation location were changed. • Wavelet and shearlet features showed the best reproducibility across all procedures.
系统评估超声(US)图像在获取和处理过程中放射组学特征的可重复性。
对标准化的体模进行扫描以获取 US 图像。通过以下方式探索 US 图像(也称为超声组学特征)的放射组学特征的可重复性:(a)在同一台 US 机器内:改变增益、焦点和频率等 US 采集参数;(b)在不同 US 机器之间:比较三种不同的扫描仪;(c)改变分割位置;以及(d)在不同平台之间:比较由 Ultrasomics 和 PyRadiomics 算法平台提取的特征。根据变异系数选择具有可重复性的超声组学特征。
从三个扫描仪获得了 108 张 US 图像;为每张 US 图像提取并评估了包括七类特征在内的 5253 个超声组学特征。在同一台 US 机器内的分析中,有 37.0-38.8%的特征表现出良好的可重复性。在不同 US 机器之间的分析中,有 42.8%(2248/5253)的特征表现出良好的可重复性。在分割位置的分析中,有 55.7-57.6%的特征表现出良好的可重复性。使用相同算法的 Ultrasomics 和 PyRadiomics 平台提取的 100 个特征之间,标准化特征范围没有显著差异(p = 0.563)。无论在改变机器内/机器间或分割位置时,有 1452 个(27.6%)超声组学特征具有可重复性,其中大多数是小波和剪切波特征。
不同的采集参数、US 扫描仪、分割位置和特征提取平台会影响超声组学特征的可重复性。在所有过程中,小波和剪切波特征的可重复性最好。
不同的采集参数、US 扫描仪、分割位置和特征提取平台会影响超声组学特征的可重复性。
无论在改变机器内/机器间或分割位置时,有 1452 个(27.6%)超声组学特征具有可重复性。
在所有过程中,小波和剪切波特征的可重复性最好。