Abstract：
OBJECTIVE: The tumor microenvironment (TME) plays a crucial role in tumor progression and treatment response. Radiomics offers a non-invasive approach to studying the TME by extracting quantitative features from medical images. In this study, we present a novel approach to assess the stability and discriminative ability of radiomics features in the TME of vestibular schwannoma (VS).
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.
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.
摘要：
目的：肿瘤微环境（TME）在肿瘤进展和治疗反应中起着至关重要的作用。Radiomics通过从医学图像中提取定量特征，提供了一种非侵入性的方法来研究TME。在这项研究中,我们提出了一种新的方法来评估前庭神经鞘瘤（VS）的TME中影像组学特征的稳定性和辨别能力。
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。
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。
