Health Physics Unit, ATS Sardinia Regional Health Service, Sassari, Italy.
Institute of Radiological Sciences, University of Sassari, Italy.
Phys Med. 2021 Feb;82:321-331. doi: 10.1016/j.ejmp.2021.02.014. Epub 2021 Mar 12.
The aim of this methods work is to explore the different behavior of radiomic features resulting by using or not the contrast medium in chest CT imaging of non-small cell lung cancer.
Chest CT scans, unenhanced and contrast-enhanced, of 17 patients were selected from images collected as part of the staging process. The major T1-T3 lesion was contoured through a semi-automatic approach. These lesions formed the lesion phantoms to study features behavior. The stability of 94 features of the 3D-Slicer package Radiomics was analyzed. Feature discrimination power was quantified by means of Gini's coefficient. Correlation between distance matrices was evaluated through Mantel statistic. Heatmap, cluster and silhouette plots were applied to find well-structured partitions of lesions.
The Gini's coefficient evidenced a low discrimination power, <0.05, for four features and a large discrimination power, around 0.8, for five features. About 90% of features was affected by the contrast medium, masking tumor lesions variability; thirteen features only were found stable. On 8178 combinations of stable features, only one group of four features produced the same partition of lesions with the silhouette width greater than 0.51, both on unenhanced and contrast-enhanced images.
Gini's coefficient highlighted the features discrimination power in both CT series. Many features were sensitive to the use of the contrast medium, masking the lesions intrinsic variability. Four stable features produced, on both series, the same partition of cancer lesions with reasonable structure; this may merit being objects of further validation studies and interpretative investigations.
本方法旨在探索在非小细胞肺癌胸部 CT 成像中使用或不使用对比剂时，放射组学特征的不同表现。
从作为分期过程一部分采集的图像中选择了 17 名患者的胸部 CT 扫描，包括未增强和增强扫描。通过半自动方法对主要 T1-T3 病变进行轮廓勾画。这些病变形成了研究特征表现的病变模型。分析了 3D-Slicer 包 Radiomics 的 94 个特征的稳定性。通过基尼系数量化特征的区分能力。通过曼特尔统计评估距离矩阵之间的相关性。应用热图、聚类和轮廓图来寻找病变的结构化分区。
基尼系数表明，有四个特征的区分能力较低（<0.05），有五个特征的区分能力较大（约 0.8）。大约 90%的特征受到对比剂的影响，掩盖了肿瘤病变的可变性；只发现了 13 个稳定的特征。在 8178 个稳定特征的组合中，只有一组四个特征在未增强和增强图像上都产生了相同的病变分区，轮廓宽度大于 0.51。
基尼系数突出了两种 CT 系列中特征的区分能力。许多特征对使用对比剂敏感，掩盖了病变的内在可变性。在两种系列中，四个稳定的特征产生了具有合理结构的相同癌症病变分区；这可能值得进一步进行验证研究和解释性研究。
