Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI.
Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI.
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DOI：
10.1038/s41598-023-37723-8
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发表时间：
2023-07-02
影响因子：
4.6
通讯作者：
Chetty, Indrin J.
中科院分区：
文献类型：
--
作者：
Bagher-Ebadian, Hassan;Brown, Stephen L.;Ghassemi, Mohammad M.;Nagaraja, Tavarekere N.;Movsas, Benjamin;Ewing, James R.;Chetty, Indrin J.
在这里，我们调查放射性组学为基础的表征肿瘤血管和微环境特性的原位大鼠脑肿瘤模型测量使用动态对比增强（DCE）MRI。使用DCE-MRI（7特斯拉，双梯度回波）对植入人U-251 N癌细胞的32只免疫受损的RNU大鼠进行成像。其目的是使用嵌套模型（NM）选择技术进行药代动力学分析，以根据被视为真实来源的血管特性对脑区域进行分类。对大鼠大脑的原始DCE-MRI进行基于二维卷积的放射组学分析，以生成动态放射组学图。原始的DCE-MRI和各自的放射组学图被用来构建28个无监督的Kohonen自组织图（K-SOM）。对K-SOM的特征空间进行轮廓系数（SC）、k倍嵌套交叉验证（k倍NCV）和特征工程分析，以量化放射组学特征与原始DCE-MRI相比的区分能力，用于不同嵌套模型的分类。结果表明，八个放射组学特征在三个嵌套模型的预测中优于各自的raw-DCE-MRI。放射组学特征和原始-DCE-MRI之间的SC的平均百分比差异为：29.875% ± 12.922%，p < 0.001。这项工作为使用放射组学特征对大脑区域进行时空表征迈出了重要的第一步，这对于肿瘤分期和评估肿瘤对不同治疗的反应至关重要。
Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs’ feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
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影响因子：
--
作者：
Zhu, Jiang;Yun, Jian;Wang, Kaixiang;Liu, Liangqing;Zheng, Jiangang;Mei, Li;Xu, Jianxing
通讯作者：
Xu, Jianxing
影响因子：
3.1
作者：
Arledge, Chad A.;Sankepalle, Deeksha M.;Crowe, William N.;Liu, Yang;Wang, Lulu;Zhao, Dawen
通讯作者：
Zhao, Dawen
DOI：
10.1002/jmri.24469
发表时间：
2014-11
期刊：
Journal of magnetic resonance imaging : JMRI
影响因子：
--
作者：
Chwang WB;Jain R;Bagher-Ebadian H;Nejad-Davarani SP;Iskander AS;VanSlooten A;Schultz L;Arbab AS;Ewing JR
通讯作者：
Ewing JR
影响因子：
3.3
作者：
Aryal, Madhava P.;Nagaraja, Tavarekere N.;Keenan, Kelly A.;Bagher-Ebadian, Hassan;Panda, Swayamprava;Brown, Stephen L.;Cabral, Glauber;Fenstermacher, Joseph D.;Ewing, James R.
通讯作者：
Ewing, James R.
影响因子：
3.3
作者：
Choi, Yangsean;Ahn, Kook-Jin;Kim, Bum-soo
通讯作者：
Kim, Bum-soo