METHODS: Magnetic Resonance Imaging (MRI) data from 242 VS patients were analyzed, including contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (hrT2) sequences. Radiomics features were extracted from concentric peri-tumoral regions of varying sizes. The intraclass correlation coefficient (ICC) was used to assess feature stability and discriminative ability, establishing quantile thresholds for ICCmin and ICCmax.
RESULTS: The identified thresholds for ICCmin and ICCmax were 0.45 and 0.72, respectively. Features were classified into four categories: stable and discriminative (S-D), stable and non-discriminative (S-ND), unstable and discriminative (US-D), and unstable and non-discriminative (US-ND). Different feature groups exhibited varying proportions of S-D features across ceT1 and hrT2 sequences. The similarity of S-D features between ceT1 and hrT2 sequences was evaluated using Jaccard\'s index, with a value of 0.78 for all feature groups which is ranging from 0.68 (intensity features) to 1.00 (Neighbouring Gray Tone Difference Matrix (NGTDM) features).
CONCLUSIONS: This study provides a framework for identifying stable and discriminative radiomics features in the TME, which could serve as potential biomarkers or predictors of patient outcomes, ultimately improving the management of VS patients.
方法:分析242例VS患者的磁共振成像(MRI)数据,包括对比增强T1加权(ceT1)和高分辨率T2加权(hrT2)序列。从不同大小的同心肿瘤周围区域提取影像组学特征。利用组内相关系数(ICC)评估特征稳定性和判别能力,为ICCmin和ICCmax建立分位数阈值。
结果:确定的ICCmin和ICCmax阈值分别为0.45和0.72。特征分为四类:稳定和判别(S-D),稳定和非判别(S-ND),不稳定和判别(US-D),以及不稳定和非判别性(US-ND)。在ceT1和hrT2序列中,不同的特征组表现出不同比例的S-D特征。使用Jaccard指数评估ceT1和hrT2序列之间的S-D特征的相似性,对于从0.68(强度特征)到1.00(相邻灰度色调差矩阵(NGTDM)特征)的所有特征组的值为0.78。
结论:这项研究提供了一个框架,用于识别TME中稳定和有区别的放射组学特征,可以作为潜在的生物标志物或患者预后的预测因子,最终改善VS患者的管理。