在我们最近的研究中，我们有效地证明了利用标准化感兴趣体积（VOI）、放射组学和机器学习将神经胶质瘤的磁共振图像（MRI）分为四种组织学类型的可行性。本研究旨在确定当 VOI 位置发生变化时我们的方法的可重复性。当在不同的 VOI 中采用相同的特征选择方法时，我们能够证明 ML 结果的高再现性。然而，对于所研究的样本量 (n = 85)，无法确保不同 VOI 之间放射组学特征及其集合的再现性。在评估神经胶质瘤的放射组学研究时，应考虑放射组学特征的有限再现性。

In our recent research, we have effectively demonstrated the feasibility of classifying magnetic resonance images (MRI) of glial tumors into four histological types utilizing standardized volume of interest (VOI), radiomics and machine learning. This research aims to determine the reproducibility of our approach when the locations of VOI are changed. We were able to demonstrate high reproducibility of ML results when the same feature selection methodology was employed across different VOIs. However, the reproducibility of radiomic features and their sets among various VOIs was not ensured for the sample size (n = 85) studied. The limited reproducibility of radiomic features should be taken into account when evaluating radiomics studies in glial tumors.