ArticleJournal of biomedical physics & engineering2025
Simplifying Radiomics Workflow for Predicting Grade of Glioma: An Approach for Rapid and Reproducible Radiomics.
Article in Journal of biomedical physics & engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Radiomics with single Region of Interest (ROI) and single-sequence Magnetic Resonance Imaging (MRI) may facilitate the segmentation reproducibility and radiomics workflow due to a time-consuming and complicated delineation of that in multi-sequence MRI images. Objective: This study aimed to evaluate the performance of the radiomics approach in grading glioma based on a single-ROI delineation as Gross Tumor Volume (GTV) in a single - sequence as contrast-enhanced T1-weighted MRI. Material and Methods: This retrospective study was conducted on contrast-enhanced T1 weighted (CE T1W) MRI images of 60 grade II and 60 grade III glioma patients. The GTV regions were manually delineated. Radiomics features were extracted per patient. The segmentation reproducibility of the robust features was evaluated in several repetitions of GTV delineation. Finally, a linear Support Vector Machine (SVM) assessed the classification performance of the robust features. Results: Four significant robust features were selected for training the model ( Conclusion: High predicting power for glioma grading can be achieved with radiomics analysis by a single-ROI delineated on a single-sequence MRI image (CE T1W). In addition, single-ROI segmentation can increase radiomics reproducibility.
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