Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
Doctoral School of Molecular Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
PLoS One. 2024 Oct 24;19(10):e0309540. doi: 10.1371/journal.pone.0309540. eCollection 2024.
Previously, we developed an "activity painting" tool for PET image simulation; however, it could simulate heterogeneous patterns only in the air. We aimed to improve this phantom technique to simulate arbitrary lesions in a radioactive background to perform relevant multi-center radiomic analysis. We conducted measurements moving a 22Na point source in a 20-liter background volume filled with 5 kBq/mL activity with an adequately controlled robotic system to prevent the surge of the water. Three different lesion patterns were "activity-painted" in five PET/CT cameras, resulting in 8 different reconstructions. We calculated 46 radiomic indeces (RI) for each lesion and imaging setting, applying absolute and relative discretization. Reproducibility and reliability were determined by the inter-setting coefficient of variation (CV) and the intraclass correlation coefficient (ICC). Hypothesis tests were used to compare RI between lesions. By simulating precisely the same lesions, we confirmed that the reconstructed voxel size and the spatial resolution of different PET cameras were critical for higher order RI. Considering conventional RIs, the SUVpeak and SUVmean proved the most reliable (CV<10%). CVs above 25% are more common for higher order RIs, but we also found that low CVs do not necessarily imply robust parameters but often rather insensitive RIs. Based on the hypothesis test, most RIs could clearly distinguish between the various lesions using absolute resampling. ICC analysis also revealed that most RIs were more reproducible with absolute discretization. The activity painting method in a real radioactive environment proved suitable for precisely detecting the radiomic differences derived from the different camera settings and texture characteristics. We also found that inter-setting CV is not an appropriate metric for analyzing RI parameters' reliability and robustness. Although multicentric cohorts are increasingly common in radiomics analysis, realistic texture phantoms can provide indispensable information on the sensitivity of an RI and how an individual RI parameter measures the texture.
此前，我们开发了一种用于 PET 图像模拟的“活动绘画”工具；然而，它只能在空气中模拟异质模式。我们旨在改进这种幻影技术，以模拟放射性背景下的任意病变，从而进行相关的多中心放射组学分析。我们使用一个经过充分控制的机器人系统来测量在充满 5 kBq/mL 活性的 20 升背景体积中移动的 22Na 点源，以防止水的涌动。我们在五台 PET/CT 相机中“活动绘画”了三种不同的病变模式，从而得到了 8 种不同的重建结果。我们为每个病变和成像设置计算了 46 个放射组学指标（RI），应用了绝对和相对离散化。通过设置间变异系数（CV）和组内相关系数（ICC）来确定可重复性和可靠性。通过假设检验来比较病变之间的 RI。通过精确模拟相同的病变，我们证实了重建体素大小和不同 PET 相机的空间分辨率对于高阶 RI 至关重要。考虑到常规 RI，SUVpeak 和 SUVmean 被证明是最可靠的（CV<10%）。高阶 RI 的 CV 通常超过 25%，但我们也发现低 CV 并不一定意味着稳健的参数，而往往是不敏感的 RI。基于假设检验，大多数 RI 可以使用绝对重采样清楚地区分各种病变。ICC 分析还表明，大多数 RI 具有绝对离散化的更高可重复性。在真实放射性环境中的活动绘画方法被证明适合于精确检测来自不同相机设置和纹理特征的放射组学差异。我们还发现，设置间 CV 不是分析 RI 参数可靠性和稳健性的适当指标。尽管放射组学分析中越来越多地使用多中心队列，但现实纹理幻影可以提供有关 RI 敏感性和单个 RI 参数如何测量纹理的不可或缺的信息。
