Foy Joseph J, Armato Samuel G, Al-Hallaq Hania A
The University of Chicago, Department of Radiology, Chicago, Illinois, United States.
The University of Chicago, Department of Radiation and Cellular Oncology, Chicago, Illinois, United States.
J Med Imaging (Bellingham). 2020 Jan;7(1):014504. doi: 10.1117/1.JMI.7.1.014504. Epub 2020 Feb 21.
While radiomics feature values can differ when extracted using different radiomics software, the effects of these variations when applied to a particular clinical task are currently unknown. The goal of our study was to use various radiomics software packages to classify patients with radiation pneumonitis (RP) and to quantify the variation in classification ability among packages. A database of serial thoracic computed tomography scans was obtained from 105 patients with esophageal cancer. Patients were treated with radiation therapy (RT), resulting in 20 patients developing RP grade . Regions of interest (ROIs) were randomly placed in the lung volume of the pre-RT scan within high-dose regions ( ), and corresponding ROIs were anatomically matched in the post-RT scan. Three radiomics packages were compared: A1 (in-house), IBEX v1.0 beta, and PyRadiomics v.2.0.0. Radiomics features robust to deformable registration and common among radiomics packages were calculated: four first-order and four gray-level co-occurrence matrix features. Differences in feature values between time points were calculated for each feature, and logistic regression was used in conjunction with analysis of variance to classify patients with and without RP ( ). Classification ability for each package was assessed using receiver operating characteristic (ROC) analysis and compared using the area under the ROC curve (AUC). Of the eight radiomics features, five were significantly correlated with RP status for all three packages, whereas one feature was not significantly correlated with RP for all three packages. The remaining two features differed in whether or not they were significantly associated with RP status among the packages. Seven of the eight features agreed among the packages in whether the AUC value was significantly . Radiomics features extracted using different software packages can result in differences in classification ability.
虽然使用不同的放射组学软件提取放射组学特征值时可能会有所不同,但目前尚不清楚这些差异应用于特定临床任务时的影响。我们研究的目的是使用各种放射组学软件包对放射性肺炎(RP)患者进行分类,并量化各软件包之间分类能力的差异。从105例食管癌患者中获得了一系列胸部计算机断层扫描数据库。患者接受放射治疗(RT),导致20例患者发生RP 级。在放疗前扫描的高剂量区域( )内的肺体积中随机放置感兴趣区域(ROI),并在放疗后扫描中进行解剖学匹配。比较了三个放射组学软件包:A1(内部开发)、IBEX v1.0 beta和PyRadiomics v.2.0.0。计算了对可变形配准具有鲁棒性且在放射组学软件包中常见的放射组学特征:四个一阶特征和四个灰度共生矩阵特征。计算每个特征在时间点之间的特征值差异,并使用逻辑回归结合方差分析对有和无RP的患者进行分类( )。使用受试者操作特征(ROC)分析评估每个软件包的分类能力,并使用ROC曲线下面积(AUC)进行比较。在八个放射组学特征中,有五个特征在所有三个软件包中与RP状态显著相关,而有一个特征在所有三个软件包中与RP均无显著相关性。其余两个特征在各软件包中与RP状态是否显著相关方面存在差异。八个特征中有七个在各软件包中关于AUC值是否显著 方面是一致的。使用不同软件包提取的放射组学特征可能会导致分类能力的差异。