Assessment of Variabilities in Lung-Contouring Methods on CBCT Preclinical Radiomics Outputs.
Assessment of Variabilities in Lung-Contouring Methods on CBCT Preclinical Radiomics Outputs.
复制标题
评估CBCT临床前放射线型输出中含有肺部肺部方法的变异性。
作者：
这项研究首次评估了轮廓差异对临床前CBCT扫描放射组学分析的影响。我们发现定量图像读数的差异在分割工具之间比在观察者之间更大。放射组学图像分析具有揭示疾病特征的潜力，可用于开发预测特征和个性化放射治疗。观察者之间和软件之间的差异已知会对放射组学特征产生下游影响，从而降低分析的可靠性。本研究的目的是研究这些变异对临床前锥形束计算机断层扫描（CBCT）放射组学输出的影响。使用手动和半自动绘制小鼠肺部轮廓来评估观察者间的变异（n = 16）。确定了两种工具（3D Slicer和ITK-SNAP）之间的软件间变异。使用Dice相似系数（DSC）评分和Hausdorff距离（HD95p）指标的第95百分位对轮廓进行比较。使用类内相关系数（ICC）及其95%置信区间定义放射组学输出的良好可靠性。DSC评分中位数很高（0.82-0.94），所有比较的HD95p指标都在亚毫米范围内。形状和NGTDM特征受影响最大。手动轮廓具有最可靠的特征（73%），其次是半自动（66%）和软件间（51%）的变化。从总共842个特征中，314个鲁棒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。
This study is the first to evaluate the impact of contouring differences on radiomics analysis in preclinical CBCT scans. We found that the variation in quantitative image readouts was greater between segmentation tools than between observers. 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.
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影响因子：
11.2
作者：
van Griethuysen JJM;Fedorov A;Parmar C;Hosny A;Aucoin N;Narayan V;Beets-Tan RGH;Fillion-Robin JC;Pieper S;Aerts HJWL
通讯作者：
Aerts HJWL
影响因子：
5.9
作者：
Ashraf H;de Hoop B;Shaker SB;Dirksen A;Bach KS;Hansen H;Prokop M;Pedersen JH
通讯作者：
Pedersen JH
影响因子：
9.3
作者：
Hatt, Mathieu;Majdoub, Mohamed;Visvikis, Dimitris
通讯作者：
Visvikis, Dimitris
影响因子：
7.7
作者：
Grossmann P;Stringfield O;El-Hachem N;Bui MM;Rios Velazquez E;Parmar C;Leijenaar RT;Haibe-Kains B;Lambin P;Gillies RJ;Aerts HJ
通讯作者：
Aerts HJ
DOI：
10.1016/j.ejmp.2019.03.024
发表时间：
2019-04-01
影响因子：
3.4
作者：
Astaraki, Mehdi;Wang, Chunliang;Smedby, Orjan
通讯作者：
Smedby, Orjan