Background/Objectives: Tumor interactions with their surrounding environment, particularly in the case of peritumoral edema, play a significant role in tumor behavior and progression. While most studies focus on the radiomic features of the tumor core, this work investigates whether peritumoral edema exhibits distinct radiomic fingerprints specific to glioma (GLI), meningioma (MEN), and metastasis (MET). By analyzing these patterns, we aim to deepen our understanding of the tumor microenvironment’s role in tumor development and progression. Methods: Radiomic features were extracted from peritumoral edema regions in T1-weighted (T1), post-gadolinium T1-weighted (T1-c), T2-weighted (T2), and T2 Fluid-Attenuated Inversion Recovery (T2-FLAIR) sequences. Three classification tasks using those features were then conducted: differentiating between Low-Grade Glioma (LGG) and High-Grade Glioma (HGG), distinguishing GLI from MET and MEN, and examining all four tumor types, i.e., LGG, HGG, MET, and MEN, to observe how tumor-specific signatures manifest in peritumoral edema. Model performance was assessed using balanced accuracy derived from 10-fold cross-validation. Results: The radiomic fingerprints specific to tumor types were more distinct in the peritumoral regions of T1-c images compared to other modalities. The best models, utilizing all features extracted from the peritumoral regions of T1-c images, achieved balanced accuracies of 0.86, 0.81, and 0.76 for the LGG-HGG, GLI-MET-MEN, and LGG-HGG-MET-MEN tasks, respectively. Conclusions: This study demonstrates that peritumoral edema, as characterized by radiomic features extracted from MRIs, contains fingerprints specific to tumor type, providing a non-invasive approach to understanding tumor-brain interactions. The results of this study hold the potential for predicting recurrence, distinguishing progression from pseudo-progression, and assessing treatment-induced changes, particularly in gliomas.
背景/目的：肿瘤与其周围环境的相互作用，尤其是在瘤周水肿的情况下，对肿瘤的行为和进展起着重要作用。尽管大多数研究聚焦于肿瘤核心的影像组学特征，但本研究旨在探讨瘤周水肿是否展现出胶质瘤（GLI）、脑膜瘤（MEN）和转移瘤（MET）特有的影像组学指纹。通过分析这些模式，我们期望加深对肿瘤微环境在肿瘤发生和进展中作用的理解。方法：从T1加权（T1）、钆增强T1加权（T1-c）、T2加权（T2）以及T2液体衰减反转恢复（T2-FLAIR）序列的瘤周水肿区域提取影像组学特征。随后利用这些特征进行了三项分类任务：区分低级别胶质瘤（LGG）与高级别胶质瘤（HGG）、区分GLI与MET和MEN，以及检验所有四种肿瘤类型（即LGG、HGG、MET和MEN），以观察肿瘤特异性特征如何在瘤周水肿中显现。模型性能通过十折交叉验证得出的平衡准确率进行评估。结果：与其他模态相比，T1-c图像的瘤周区域中肿瘤类型特有的影像组学指纹更为显著。利用从T1-c图像瘤周区域提取的所有特征构建的最佳模型，在LGG-HGG、GLI-MET-MEN和LGG-HGG-MET-MEN分类任务中分别实现了0.86、0.81和0.76的平衡准确率。结论：本研究表明，通过MRI提取的影像组学特征所表征的瘤周水肿包含肿瘤类型特有的指纹，为理解肿瘤与脑组织的相互作用提供了一种非侵入性方法。该研究结果在预测复发、区分进展与假性进展以及评估治疗引起的变化（尤其在胶质瘤中）方面具有潜在应用价值。
Radiomic Fingerprinting of the Peritumoral Edema in Brain Tumors
肿瘤(癌症)患者之家