Assessing the stability and discriminative ability of radiomics features in the tumor microenvironment: Leveraging peri-tumoral regions in vestibular schwannoma

• Published in: European journal of radiology 2024-09, Vol.178, p.111654, Article 111654
• Main Authors: Hosseini, Mahboube Sadat, Aghamiri, Seyed Mahmoud Reza, Fatemi Ardekani, Ali, BagheriMofidi, Seyed Mehdi
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Contrast Media; Female; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Middle Aged; Neuroma, Acoustic - diagnostic imaging; Peri-tumoral regions; Radiomics; Retrospective Studies; Stability analysis; Tumor Microenvironment; Vestibular Schwannoma; Young Adult
• ISSN: 0720-048X; 1872-7727; 1872-7727
• DOI: 10.1016/j.ejrad.2024.111654

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). 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. 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). 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.