Department of Physics, University of South Florida, Tampa, FL, 33620, USA.
Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
Sci Rep. 2018 Jul 12;8(1):10545. doi: 10.1038/s41598-018-28895-9.
Radiomic features are potential imaging biomarkers for therapy response assessment in oncology. However, the robustness of features with respect to imaging parameters is not well established. Previously identified potential imaging biomarkers were found to be intrinsically dependent on voxel size and number of gray levels (GLs) in a recent texture phantom investigation. Here, we validate the voxel size and GL in-phantom normalizations in lung tumors. Eighteen patients with non-small cell lung cancer of varying tumor volumes were analyzed. To compare with patient data, phantom scans were acquired on eight different scanners. Twenty four previously identified features were extracted from lung tumors. The Spearman rank (r) and interclass correlation coefficient (ICC) were used as metrics. Eight out of 10 features showed high (r > 0.9) and low (r < 0.5) correlations with number of voxels before and after normalizations, respectively. Likewise, texture features were unstable (ICC < 0.6) and highly stable (ICC > 0.8) before and after GL normalizations, respectively. We conclude that voxel size and GL normalizations derived from a texture phantom study also apply to lung tumors. This study highlights the importance and utility of investigating the robustness of radiomic features with respect to CT imaging parameters in radiomic phantoms.
影像组学特征是肿瘤治疗反应评估的潜在影像学生物标志物。然而，特征对成像参数的稳健性尚未得到很好的确定。在最近的纹理体模研究中发现，以前确定的潜在影像学生物标志物本质上依赖于体素大小和灰度级（GL）的数量。在此，我们验证了肺肿瘤中体素大小和 GL 的体模归一化。分析了 18 名不同肿瘤体积的非小细胞肺癌患者。为了与患者数据进行比较，在 8 台不同的扫描仪上采集了体模扫描。从肺肿瘤中提取了 24 个先前确定的特征。使用 Spearman 秩相关系数（r）和组内相关系数（ICC）作为度量标准。10 个特征中有 8 个在归一化前后与体素数量分别具有高（r＞0.9）和低（r＜0.5）相关性。同样，纹理特征在 GL 归一化前后分别不稳定（ICC＜0.6）和高度稳定（ICC＞0.8）。我们得出结论，从纹理体模研究中得出的体素大小和 GL 归一化也适用于肺肿瘤。这项研究强调了在放射组学体模中研究放射组学特征对 CT 成像参数的稳健性的重要性和实用性。
