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首页> 外文期刊>Physics and Imaging in Radiation Oncology >Automated detection of dental artifacts for large-scale radiomic analysis in radiation oncology
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Automated detection of dental artifacts for large-scale radiomic analysis in radiation oncology

机译：辐射肿瘤学中大型射系分析的牙科伪影自动检测

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Background and purpose Computed tomography (CT) is one of the most common medical imaging modalities in radiation oncology and radiomics research, the computational voxel-level analysis of medical images. Radiomics is vulnerable to the effects of dental artifacts (DA) caused by metal implants or fillings and can hamper future reproducibility on new datasets. In this study we seek to better understand the robustness of quantitative radiomic features to DAs. Furthermore, we propose a novel method of detecting DAs in order to safeguard radiomic studies and improve reproducibility. Materials and methods We analyzed the correlations between radiomic features and the location of dental artifacts in a new dataset containing 3D CT scans from 3211 patients. We then combined conventional image processing techniques with a pre-trained convolutional neural network to create a three-class patient-level DA classifier and slice-level DA locator. Finally, we demonstrated its utility in reducing the correlations between the location of DAs and certain radiomic features. Results We found that when strong DAs were present, the proximity of the tumour to the mouth was highly correlated with 36 radiomic features. We predicted the correct DA magnitude yielding a Matthews correlation coefficient of 0.73 and location of DAs achieving the same level of agreement as human labellers. Conclusions Removing radiomic features or CT slices containing DAs could reduce the unwanted correlations between the location of DAs and radiomic features. Automated DA detection can be used to improve the reproducibility of radiomic studies; an important step towards creating effective radiomic models for use in clinical radiation oncology.
机译：背景和目的计算机断层扫描（CT）是辐射肿瘤学和辐射瘤研究中最常见的医学成像方式之一，医学图像的计算体素级分析。辐射瘤容易受到金属植入物或填充引起的牙科工件（DA）的影响，并且可以在新数据集中妨碍未来的再现性。在这项研究中，我们寻求更好地理解定量射出物特征对DAS的鲁棒性。此外，我们提出了一种检测DAS的新方法，以保护射出物研究并提高再现性。我们分析了从3211名患者的3D CT扫描的新数据集中的射出数据特征与牙科伪影的位置之间的相关性。然后，我们将传统的图像处理技术与预先训练的卷积神经网络组合以创建三类患者级DA分类器和切片级DA定位器。最后，我们展示了它在降低DAS位置与某些射出物特征之间的相关性的实用性。结果发现，当存在强烈的DAS时，肿瘤与口腔的接近与36个射系特征高度相关。我们预测了正确的DA幅度，产生Matthews相关系数为0.73，并且DAS的位置实现与人类兰布斯相同的协议。结论去除含有DAS的射出物特征或CT切片可以降低DAS和射出物特征的位置之间的不希望的相关性。自动化DA检测可用于提高射出物研究的再现性;促进用于临床放射肿瘤学的有效射粒模型的重要一步。

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