Drayson Olivia Gg, Gruel Pierre-Montay, Limoli Charles L
University of California, Irvine.
University of Antwerp.
Res Sq. 2024 Feb 23:rs.3.rs-3951996. doi: 10.21203/rs.3.rs-3951996/v1.
Radiomic features were used in efforts to characterize radiation-induced normal tissue injury as well as identify if human embryonic stem cell (hESC) derived Extracellular Vesicle (EV) treatment could resolve certain adverse complications. A cohort of mice (n=12/group) were given whole lung irradiation (3×8Gy), local irradiation to the right lung apex (3×12Gy), or no irradiation. The hESC-derived EVs were systemically administered three times via retro-orbital injection immediately after each irradiation. Cone-Beam Computed Tomography (CBCT) images were acquired at baseline and 2 weeks after the final radiation/EV treatment. Whole lung image segmentation was performed and radiomic features were extracted with wavelet filtering applied. A total of 851 features were extracted per image and recursive feature elimination was used to refine, train and validate a series of random forest classification models. Classification models trained to identify irradiated from unirradiated animals or EV treated from vehicle-injected animals achieved high prediction accuracies (94% and 85%). In addition, radiomic features from the locally irradiated dataset showed significant radiation impact and EV sparing effects that were absent in the unirradiated left lung. Our data demonstrates that radiomics has the potential to characterize radiation-induced lung injury and identify therapeutic efficacy at early timepoints.
放射组学特征被用于表征辐射诱导的正常组织损伤，以及确定人胚胎干细胞（hESC）衍生的细胞外囊泡（EV）治疗是否可以解决某些不良并发症。将一组小鼠（每组n = 12只）进行全肺照射（3×8Gy）、右肺尖局部照射（3×12Gy）或不进行照射。在每次照射后立即通过眶后注射将hESC衍生的EV全身给药三次。在基线以及最终放疗/EV治疗后2周采集锥形束计算机断层扫描（CBCT）图像。进行全肺图像分割，并应用小波滤波提取放射组学特征。每张图像共提取851个特征，并使用递归特征消除来优化、训练和验证一系列随机森林分类模型。训练用于识别辐照动物与未辐照动物或EV治疗动物与注射载体动物的分类模型取得了较高的预测准确率（分别为94%和85%）。此外，局部照射数据集的放射组学特征显示出明显的辐射影响和EV保护作用，而未辐照的左肺则没有这些作用。我们的数据表明，放射组学有潜力表征辐射诱导的肺损伤，并在早期时间点识别治疗效果。
