Institute of Biostructures and Bioimaging, National Research Council (CNR), Via Tommaso de Amicis, 95, 80145, Napoli, Italy.
Jpn J Radiol. 2021 Jul;39(7):710-719. doi: 10.1007/s11604-021-01100-0. Epub 2021 Feb 17.
To compare texture feature estimates obtained from F-FDG-PET images using three different software packages.
PET images from 15 patients with head and neck cancer were processed with three different freeware software: CGITA, LIFEx, and Metavol. For each lesion, 38 texture features were extracted from each software package. To evaluate the statistical agreement among the features across packages a non-parametric Kruskal-Wallis test was used. Differences in the features between each couple of software were assessed using a subsequent Dunn test. Correlation between texture features was evaluated via the Spearman coefficient.
Twenty-three of 38 features showed a significant agreement across the three software (P < 0.05). The agreement was better between LIFEx vs. Metavol (36 of 38) and worse between CGITA and Metavol (24 of 38), and CGITA vs. LIFEx (23 of 38). All features resulted correlated (ρ > = 0.70, P < 0.001) in comparing LIFEx vs. Metavol. Seven of 38 features were found not in agreement and slightly or not correlated (ρ < 0.70, P < 0.001) in comparing CGITA vs. LIFEx, and CGITA vs. Metavol.
Some texture discrepancies across software packages exist. Our findings reinforce the need to continue the standardization process, and to succeed in building a reference dataset to be used for comparisons.
比较三种不同软件包从 F-FDG-PET 图像中提取的纹理特征估计值。
对头颈癌患者的 15 例 PET 图像进行处理,使用三种不同的免费软件:CGITA、LIFEx 和 Metavol。对于每个病灶,从每个软件包中提取 38 个纹理特征。为了评估特征在软件包之间的统计一致性,使用非参数 Kruskal-Wallis 检验。使用后续 Dunn 检验评估软件之间特征的差异。通过 Spearman 系数评估纹理特征之间的相关性。
38 个特征中有 23 个在三个软件之间具有显著一致性(P < 0.05)。LIFEx 与 Metavol 之间的一致性更好(36 个),CGITA 与 Metavol 之间的一致性更差(24 个),CGITA 与 LIFEx 之间的一致性最差(23 个)。在比较 LIFEx 与 Metavol 时,所有特征均呈高度相关(ρ> = 0.70,P < 0.001)。在比较 CGITA 与 LIFEx 时,有 7 个特征不一致,且相关性较弱或不相关(ρ< 0.70,P < 0.001);在比较 CGITA 与 Metavol 时也存在类似情况。
不同软件包之间存在一些纹理差异。我们的发现强调了需要继续进行标准化过程,并成功构建用于比较的参考数据集。