ArticlePloS one2017
Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
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- Machine Learning and Radiomics in Gliomas.Advances in experimental medicine and biology · 2024Review
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- Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives.View (Beijing, China) · 2023Article
- Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities.Frontiers in medicine · 2023Article
- Imaging in translational cancer research.Cancer biology & medicine · 2022Review
- Uniqueness of radiomic features in non-small cell lung cancer.Journal of applied clinical medical physics · 2022Article
- AutoComBat: a generic method for harmonizing MRI-based radiomic features.Scientific reports · 2022Article
- Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy.Scientific reports · 2022Article
- The R-AI-DIOLOGY checklist: a practical checklist for evaluation of artificial intelligence tools in clinical neuroradiology.Neuroradiology · 2022Review
- Challenges in ensuring the generalizability of image quantitation methods for MRI.Medical physics · 2022Review
- Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters.Journal of magnetic resonance imaging : JMRI · 2021Article
- Radiomics and radiogenomics in gliomas: a contemporary update.British journal of cancer · 2021Review
- Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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7 authors.
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Abstract
purposeTextural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols. MATERIALS AND
methodsTwenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study. Four different spatial resolution combinations and three dynamic ranges were studied for each patient. Sixteen three-dimensional textural heterogeneity measures were computed for each patient and configuration including co-occurrence matrices (CM) features and run-length matrices (RLM) features. The coefficient of variation was used to assess the robustness of the measures in two series of experiments corresponding to (i) changing the dynamic range and (ii) changing the matrix size.
resultsNo textural measures were robust under dynamic range changes. Entropy was the only textural feature robust under spatial resolution changes (coefficient of variation under 10% in all cases).
conclusionTextural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes. Standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies. The implications of this work go beyond the specific tumor type studied here and pose the need for standardization in textural feature calculation of oncological images.
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