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影像组学特征是否普遍适用于不同器官?

Are radiomics features universally applicable to different organs?

机构信息

Departement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea.

Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, South Korea.

出版信息

Cancer Imaging. 2021 Apr 7;21(1):31. doi: 10.1186/s40644-021-00400-y.

Abstract

BACKGROUND

Many studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common radiomics features could be identified over target organs in vastly different environments.

METHODS

Four datasets of three organs were analyzed. One radiomics model was constructed from the training set (lungs, n = 401), and was further evaluated in three independent test sets spanning three organs (lungs, n = 59; kidneys, n = 48; and brains, n = 43). Intensity histograms derived from the whole organ were compared to establish organ-level differences. We constructed a radiomics score based on selected features using training lung data over the tumor region. A total of 143 features were computed for each tumor. We adopted a feature selection approach that favored stable features, which can also capture survival. The radiomics score was applied to three independent test data from lung, kidney, and brain tumors, and whether the score could be used to separate high- and low-risk groups, was evaluated.

RESULTS

Each organ showed a distinct pattern in the histogram and the derived parameters (mean and median) at the organ-level. The radiomics score trained from the lung data of the tumor region included seven features, and the score was only effective in stratifying survival for other lung data, not in other organs such as the kidney and brain. Eliminating the lung-specific feature (2.5 percentile) from the radiomics score led to similar results. There were no common features between training and test sets, but a common category of features (texture category) was identified.

CONCLUSION

Although the possibility of a generally applicable model cannot be excluded, we suggest that radiomics score models for survival were mostly specific for a given organ; applying them to other organs would require careful consideration of organ-specific properties.

摘要

背景

许多研究已经成功地确定了反映各种器官宏观肿瘤特征和肿瘤微环境的放射组学特征。人们越来越感兴趣的是将在特定器官中发现的这些放射组学特征应用于其他器官。在这里,我们探讨了是否可以在差异极大的目标器官中识别出常见的放射组学特征。

方法

分析了四个器官的四个数据集。从训练集(n=401)构建了一个放射组学模型,并在三个独立的测试集(n=59)中进行了进一步评估:肺部、n=48;肾脏和 n=43;脑部。比较来自整个器官的强度直方图,以确定器官水平的差异。我们基于训练肺部肿瘤区域的数据构建了一个放射组学评分,该评分基于选定的特征。每个肿瘤计算了 143 个特征。我们采用了一种有利于稳定特征的特征选择方法,这种方法还可以捕获生存信息。将放射组学评分应用于来自肺部、肾脏和脑部肿瘤的三个独立测试数据,评估评分是否可用于区分高风险和低风险组。

结果

每个器官在直方图和衍生参数(均值和中位数)方面都显示出独特的模式。从肺部肿瘤区域数据训练的放射组学评分包括七个特征,该评分仅在肺部其他数据中有效,可以对生存进行分层,而在其他器官(如肾脏和大脑)中则无效。从放射组学评分中删除肺部特有的特征(2.5 分位数)会导致类似的结果。在训练集和测试集中没有共同的特征,但识别出了一种共同的特征类别(纹理类别)。

结论

虽然不能排除普遍适用模型的可能性,但我们建议,用于生存的放射组学评分模型大多是特定于特定器官的;将其应用于其他器官需要仔细考虑器官特异性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9b3/8028225/395ce7ecc77b/40644_2021_400_Fig1_HTML.jpg

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