ArticleMagma (New York, N.Y.)2023
The impact of pre-processing and disease characteristics on reproducibility of T2-weighted MRI radiomics features.
Article in Magma (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Article
- Influence of image preprocessing on reproducibility and longitudinal repeatability analysis of radiomics features in magnetic resonance image-guided accelerator imaging.Quantitative imaging in medicine and surgery · 2026Article
- Addressing the current challenges in the clinical application of AI-based Radiomics for cancer imaging.Frontiers in medicine · 2025Review
- Exploring the value of multiple preprocessors and classifiers in constructing models for predicting microsatellite instability status in colorectal cancer.Scientific reports · 2024Article
- Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI.Diagnostics (Basel, Switzerland) · 2024Review
- Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.Cancers · 2024Review
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Authors and funding
9 authors.
Funding
Abstract
purposeTo evaluate the reproducibility of radiomics features derived via different pre-processing settings from paired T2-weighted imaging (T2WI) prostate lesions acquired within a short interval, to select the setting that yields the highest number of reproducible features, and to evaluate the impact of disease characteristics (i.e., clinical variables) on features reproducibility. MATERIALS AND
methodsA dataset of 50 patients imaged using T2WI at 2 consecutive examinations was used. The dataset was pre-processed using 48 different settings. A total of 107 radiomics features were extracted from manual delineations of 74 lesions. The inter-scan reproducibility of each feature was measured using the intra-class correlation coefficient (ICC), with ICC values > 0.75 considered good. Statistical differences were assessed using Mann-Whitney U and Kruskal-Wallis tests.
resultsThe pre-processing parameters strongly influenced the reproducibility of radiomics features of T2WI prostate lesions. The setting that yielded the highest number of features (25 features) with high reproducibility was the relative discretization with a fixed bin number of 64, no signal intensity normalization, and outlier filtering by excluding outliers. Disease characteristics did not significantly impact the reproducibility of radiomics features.
conclusionThe reproducibility of T2WI radiomics features was significantly influenced by pre-processing parameters, but not by disease characteristics. The selected pre-processing setting yielded 25 reproducible features.
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