A Nanoradiomics Approach for Differentiation of Tumors Based on Tumor-Associated Macrophage Burden.
A Nanoradiomics Approach for Differentiation of Tumors Based on Tumor-Associated Macrophage Burden.
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DOI：
10.1155/2021/6641384
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发表时间：
2021
影响因子：
--
通讯作者：
Ghaghada KB
中科院分区：
文献类型：
--
作者：
Starosolski Z;Courtney AN;Srivastava M;Guo L;Stupin I;Metelitsa LS;Annapragada A;Ghaghada KB
实体瘤的肿瘤免疫微环境（TiME）中的肿瘤相关巨噬细胞（TAM）在治疗抵抗和疾病复发中起重要作用。本研究的目的是研究纳米放射组学（纳米颗粒对比增强图像的放射组学分析）是否可以根据TAM负荷区分肿瘤。 在具有低（N = 11）和高（N = 10）肿瘤相关巨噬细胞（TAM）负荷的神经母细胞瘤转基因小鼠模型中进行体内研究。在静脉内施用脂质体-碘剂（1.1g/kg）后4天，动物经历延迟纳米颗粒对比增强CT（n-CECT）成像。针对分割的肿瘤CT数据集计算CT成像衍生的常规肿瘤度量（肿瘤体积和CT衰减）。使用在包含900个放射组学特征（RF）的定量图像特征管道（QIFP）服务器中实现的PyRadiomics工作流进行纳米放射组学分析。使用非参数邻域分量法在监督机器学习下进行RF选择。使用一组用于组分离的线性和非线性分类器进行5倍验证。使用Kruskal-Wallis检验进行统计分析。 N-CECT成像显示低和高TAM肿瘤的信号增强模式不均匀。CT成像衍生的常规肿瘤度量显示低和高TAM肿瘤之间的肿瘤体积没有显著差异（p > 0.05）。肿瘤CT衰减在低TAM和高TAM肿瘤之间没有显著差异（p > 0.05）。机器学习增强的纳米放射组学分析揭示了两种区分（p < 0.002）低TAM和高TAM肿瘤的RF。使用RF来构建线性分类器，该线性分类器表现出非常高的准确性，并通过5倍交叉验证进一步证实。 成像衍生的常规肿瘤指标无法区分具有不同TAM负荷的肿瘤;然而，纳米放射组学分析揭示了纹理差异并能够区分低和高TAM肿瘤。
Tumor-associated macrophages (TAMs) within the tumor immune microenvironment (TiME) of solid tumors play an important role in treatment resistance and disease recurrence. The purpose of this study was to investigate if nanoradiomics (radiomic analysis of nanoparticle contrast-enhanced images) can differentiate tumors based on TAM burden. In vivo studies were performed in transgenic mouse models of neuroblastoma with low (N = 11) and high (N = 10) tumor-associated macrophage (TAM) burden. Animals underwent delayed nanoparticle contrast-enhanced CT (n-CECT) imaging at 4 days after intravenous administration of liposomal-iodine agent (1.1 g/kg). CT imaging-derived conventional tumor metrics (tumor volume and CT attenuation) were computed for segmented tumor CT datasets. Nanoradiomic analysis was performed using a PyRadiomics workflow implemented in the quantitative image feature pipeline (QIFP) server containing 900 radiomic features (RFs). RF selection was performed under supervised machine learning using a nonparametric neighborhood component method. A 5-fold validation was performed using a set of linear and nonlinear classifiers for group separation. Statistical analysis was performed using the Kruskal–Wallis test. N-CECT imaging demonstrated heterogeneous patterns of signal enhancement in low and high TAM tumors. CT imaging-derived conventional tumor metrics showed no significant differences (p > 0.05) in tumor volume between low and high TAM tumors. Tumor CT attenuation was not significantly different (p > 0.05) between low and high TAM tumors. Machine learning-augmented nanoradiomic analysis revealed two RFs that differentiated (p < 0.002) low TAM and high TAM tumors. The RFs were used to build a linear classifier that demonstrated very high accuracy and further confirmed by 5-fold cross-validation. Imaging-derived conventional tumor metrics were unable to differentiate tumors with varying TAM burden; however, nanoradiomic analysis revealed texture differences and enabled differentiation of low and high TAM tumors.
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影响因子：
50.3
作者：
Lewis CE;Harney AS;Pollard JW
通讯作者：
Pollard JW
影响因子：
32.4
作者：
Mendiratta, SK;Martin, WD;VanKaer, L
通讯作者：
VanKaer, L
影响因子：
82.9
作者：
Binnewies M;Roberts EW;Kersten K;Chan V;Fearon DF;Merad M;Coussens LM;Gabrilovich DI;Ostrand-Rosenberg S;Hedrick CC;Vonderheide RH;Pittet MJ;Jain RK;Zou W;Howcroft TK;Woodhouse EC;Weinberg RA;Krummel MF
通讯作者：
Krummel MF
影响因子：
11.2
作者：
Hughes R;Qian BZ;Rowan C;Muthana M;Keklikoglou I;Olson OC;Tazzyman S;Danson S;Addison C;Clemons M;Gonzalez-Angulo AM;Joyce JA;De Palma M;Pollard JW;Lewis CE
通讯作者：
Lewis CE
影响因子：
4.8
作者：
Ghaghada, Ketan B.;Badea, Cristian T.;Karumbaiah, Lohitash;Fettig, Nicole;Bellamkonda, Ravi V.;Johnson, G. A.;Annapragada, Ananth
通讯作者：
Annapragada, Ananth