Unit of Radiation Oncology, Ospedale del Mare, 80147, Naples, Italy.
Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
Med Oncol. 2020 Mar 31;37(5):38. doi: 10.1007/s12032-020-01359-9.
Texture analysis (TA) can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols. Delta-texture analysis (D-TA), conversely, consist in the analysis of TA feature variations at different acquisition times, usually before and after a therapy. Aim of this study was to investigate the influence of different CT scanners and acquisition parameters in the robustness of TA and D-TA. We scanned a commercial phantom (CIRS model 467, Gammex, Middleton, WI, USA), that is used for the calibration of electron density, two times by varying the disposition of plugs, using three different scanners. After the segmentation, we extracted TA features with LifeX and calculated TA features and D-TA features, defined as the variation of each TA parameters extracted from the same position by varying the plugs with the formula (Y-X)/X. The robustness of TA and D-TA features were then tested with intraclass coefficient correlation (ICC) analysis. The reliability of TA parameters across different scans, with different acquisition parameters and ROI positions has shown poor reliability in 12/37 and moderate reliability in the remaining 25/37, with no parameters showing good reliability. The reliability of D-TA, conversely, showed poor reliability in 10/37 parameters, moderate reliability in 10/37 parameters, and good reliability in 17/37 parameters. The comparison between TA and D-TA ICCs showed a significant difference for the whole group of parameters (p:0.004) and for the subclasses of GLCM parameters (p:0.033), whereas for the other subclasses of matrices (GLRLM, NGLDM, GLZLM, Histogram), the difference was not significant. D-TA features seem to be more robust than TA features. These findings reinforce the potentiality for using D-TA features for early assessment of treatment response and for developing tailored therapies. More work is needed in a clinical setting to confirm the results of the present study.
纹理分析（TA）可以从医学影像中提供定量特征，这些特征可以与临床终点相关联。许多研究人员已经分析了与放射组学特征稳健性相关的挑战，因为它似乎受到采集和重建协议的影响。相反，差值纹理分析（D-TA）是指在不同采集时间（通常是治疗前后）分析 TA 特征的变化。本研究旨在研究不同 CT 扫描仪和采集参数对 TA 和 D-TA 稳健性的影响。我们使用三个不同的扫描仪两次扫描了一个商业体模（CIRS 模型 467，Gammex，Middleton，WI，USA），该体模用于电子密度校准。在分割后，我们使用 LifeX 提取 TA 特征，并计算 TA 特征和 D-TA 特征，定义为通过改变插件从同一位置提取的每个 TA 参数的变化，公式为（Y-X）/X。然后通过 ICC 分析测试 TA 和 D-TA 特征的稳健性。在不同扫描、不同采集参数和 ROI 位置下 TA 参数的可靠性显示 12/37 个参数的可靠性较差，25/37 个参数的可靠性中等，没有参数的可靠性良好。相反，D-TA 的可靠性显示 10/37 个参数的可靠性较差，10/37 个参数的可靠性中等，17/37 个参数的可靠性良好。TA 和 D-TA ICC 之间的比较显示整个参数组（p：0.004）和 GLCM 参数子类（p：0.033）之间存在显著差异，而对于其他矩阵子类（GLRLM、NGLDM、GLZLM、Histogram），差异不显著。D-TA 特征似乎比 TA 特征更稳健。这些发现增强了使用 D-TA 特征进行治疗反应早期评估和开发定制治疗的潜力。需要在临床环境中进行更多工作来证实本研究的结果。
