Department of Medical Radiation Physics, Clinical Sciences, 5193Lund University, Lund, Sweden.
Radiation Oncology Department, 25301H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Technol Cancer Res Treat. 2022 Jan-Dec;21:15330338221099113. doi: 10.1177/15330338221099113.
Radiomics entails the extraction of quantitative imaging biomarkers (or radiomics features) hypothesized to provide additional pathophysiological and/or clinical information compared to qualitative visual observation and interpretation. This retrospective study explores the variability of radiomics features extracted from images acquired with the 0.35 T scanner of an integrated MRI-Linac. We hypothesized we would be able to identify features with high repeatability and reproducibility over various imaging conditions using phantom and patient imaging studies. We also compared findings from the literature relevant to our results. Eleven scans of a Magphan RT phantom over 13 months and 11 scans of a ViewRay Daily QA phantom over 11 days constituted the phantom data. Patient datasets included 50 images from ten anonymized stereotactic body radiation therapy (SBRT) pancreatic cancer patients (50 Gy in 5 fractions). A True Fast Imaging with Steady-State Free Precession (TRUFI) pulse sequence was selected, using a voxel resolution of 1.5 mm × 1.5 mm × 1.5 mm and 1.5 mm × 1.5 mm × 3.0 mm for phantom and patient data, respectively. A total of 1087 shape-based, first, second, and higher order features were extracted followed by robustness analysis. Robustness was assessed with the Coefficient of Variation (CoV < 5%). We identified 130 robust features across the datasets. Robust features were found within each category, except for 2 second-order sub-groups, namely, Gray Level Size Zone Matrix (GLSZM) and Neighborhood Gray Tone Difference Matrix (NGTDM). Additionally, several robust features agreed with findings from other stability assessments or predictive performance studies in the literature. We verified the stability of the 0.35 T scanner of an integrated MRI-Linac for longitudinal radiomics phantom studies and identified robust features over various imaging conditions. We conclude that phantom measurements can be used to identify robust radiomics features. More stability assessment research is warranted.
放射组学需要提取定量成像生物标志物（或放射组学特征），与定性视觉观察和解释相比，这些特征假设可以提供额外的病理生理和/或临床信息。这项回顾性研究探索了从集成 MRI-直线加速器的 0.35T 扫描仪获得的图像中提取的放射组学特征的可变性。我们假设能够在各种成像条件下使用体模和患者成像研究来识别具有高可重复性和可再现性的特征。我们还比较了与我们的结果相关的文献中的发现。 11 个 Magphan RT 体模在 13 个月内和 11 个 ViewRay Daily QA 体模在 11 天内进行的扫描构成了体模数据。患者数据集包括来自十个匿名立体定向体部放射治疗（SBRT）胰腺癌患者的 50 个图像（50Gy 分 5 次）。选择了 True Fast Imaging with Steady-State Free Precession（TRUFI）脉冲序列，体模数据的体素分辨率为 1.5mm×1.5mm×1.5mm，患者数据的体素分辨率为 1.5mm×1.5mm×3.0mm。提取了总共 1087 个基于形状的、一阶、二阶和更高阶特征，然后进行稳健性分析。稳健性用变异系数（CoV<5%）评估。 我们在数据集之间识别了 130 个稳健的特征。除了两个二阶子组，即灰度大小区域矩阵（GLSZM）和邻域灰度差矩阵（NGTDM）外，每个类别都找到了稳健的特征。此外，一些稳健的特征与文献中其他稳定性评估或预测性能研究的发现一致。 我们验证了集成 MRI-直线加速器的 0.35T 扫描仪在纵向放射组学体模研究中的稳定性，并在各种成像条件下识别了稳健的特征。我们得出结论，体模测量可用于识别稳健的放射组学特征。需要进行更多的稳定性评估研究。
