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  • 1From the Department of Mathematics (M.N.), School of Science and Engineering; department of Mathematics, Computer Science, and Physics (A.S.), University of Udine; The Roy J. Carver Department of Biomedical Engineering (M.F.A.C.), The University of Iowa; and Department of Electrical Engineering, School of Science and Engineering (H.M.-uD.), LUMS.

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Highly stable radiomics features, primarily texture-based from whole tumor regions, improve brain tumor characterization models. This stability enhances the generalizability of radiogenomics predictions, leading to more robust diagnostic tools.

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Area of Science:

  • Radiomics and Medical Imaging
  • Artificial Intelligence in Oncology
  • Neuro-oncology

Background:

  • Brain tumor characterization relies on accurate segmentation and feature extraction.
  • Variability in automated segmentation can impact radiomics feature stability.
  • The generalizability of radiogenomics models is crucial for clinical application.

Purpose of the Study:

  • To assess the stability of radiomics features derived from fully-automatic deep segmentation masks in brain tumors.
  • To evaluate the impact of feature stability on downstream prediction tasks, specifically IDH mutation prediction.
  • To determine if stable and discriminatory radiomics features can lead to generalizable radiogenomics models.

Main Methods:

  • Utilized the BraTS 2020 dataset for multiparametric 3D MRI segmentation and IDH prediction.
  • Employed seven state-of-the-art Convolutional Neural Networks (CNNs) for fully automatic multi-regional tumor segmentation.
  • Assessed radiomics feature stability using Overall Concordance Correlation Coefficient (OCCC) and selected discriminatory features via RFE-SVM.

Main Results:

  • Highly stable radiomics features were predominantly texture-based (79.1%), mainly from the whole tumor (WT) region (96.1%).
  • Feature stability was highest for WT (OCCC: 0.87 ± 0.12), followed by tumor core (TC) and enhancing core (EC).
  • Stability filtering significantly improved predictive performance (AUC: 0.81 ± 0.02 to 0.94 ± 0.006) and reduced variability (RSD: 2.28% to 0.64%).

Conclusions:

  • Robust and generalizable radiogenomics models can be developed using stable and discriminatory radiomics features.
  • Feature stability is a critical factor for reliable brain tumor characterization and prediction.
  • The findings support the use of stable radiomics features for building trustworthy AI-driven diagnostic tools.