PDF(Pubmed)
Abstract：
Radiomics image analysis has the potential to uncover disease characteristics for the development of predictive signatures and personalised radiotherapy treatment. Inter-observer and inter-software delineation variabilities are known to have downstream effects on radiomics features, reducing the reliability of the analysis. The purpose of this study was to investigate the impact of these variabilities on radiomics outputs from preclinical cone-beam computed tomography (CBCT) scans. Inter-observer variabilities were assessed using manual and semi-automated contours of mouse lungs (n = 16). Inter-software variabilities were determined between two tools (3D Slicer and ITK-SNAP). The contours were compared using Dice similarity coefficient (DSC) scores and the 95th percentile of the Hausdorff distance (HD95p) metrics. The good reliability of the radiomics outputs was defined using intraclass correlation coefficients (ICC) and their 95% confidence intervals. The median DSC scores were high (0.82-0.94), and the HD95p metrics were within the submillimetre range for all comparisons. the shape and NGTDM features were impacted the most. Manual contours had the most reliable features (73%), followed by semi-automated (66%) and inter-software (51%) variabilities. From a total of 842 features, 314 robust features overlapped across all contouring methodologies. In addition, our results have a 70% overlap with features identified from clinical inter-observer studies.
摘要：
放射组学图像分析有可能发现疾病特征，以开发预测性特征和个性化放射治疗。已知观察者间和软件间描述变量对影像组学特征有下游影响，降低了分析的可靠性。这项研究的目的是研究这些变化对临床前锥形束计算机断层扫描（CBCT）扫描的影像组学输出的影响。使用小鼠肺的手动和半自动轮廓评估观察者间的变异性（n=16）。在两个工具(3DSlicer和ITK-SNAP)之间确定软件间可变性。使用Dice相似性系数（DSC）得分和Hausdorff距离（HD95p）度量的第95百分位数比较轮廓。使用组内相关系数（ICC）及其95％置信区间定义了影像组学输出的良好可靠性。DSC评分中位数较高（0.82-0.94），所有比较的HD95p指标都在亚毫米范围内。形状和NGTDM特征受到的影响最大。手动轮廓具有最可靠的功能（73％），其次是半自动(66%)和软件间(51%)变化。从总共842个功能中，314个健壮特征在所有轮廓方法中重叠。此外,我们的结果与临床观察者间研究确定的特征有70%的重叠.
