ArticleEuropean radiology2021
How can we combat multicenter variability in MR radiomics? Validation of a correction procedure.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 3 of them syntheses that pooled it.
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Who cites it
135 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Pooled it
- Methodological issues in radiomics: impact on accuracy of MRI for predicting response to neoadjuvant chemotherapy in breast cancer.European radiology · 2025Pooled it
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Article
- Impact of different pre-processing methods combined with combat harmonization on the reproducibility of radiomics features in healthy human brain MRI.Magma (New York, N.Y.) · 2026Article
- Validating Radiomic Feature Comparisons with Patient-Specific Image Surrogates.Journal of imaging informatics in medicine · 2026Article
- Transparent, Reproducible Text-Based Phenotyping of Lumbar Intervertebral Disc Degeneration from 500 Consecutive MRI Reports, with a Pre-Specified Image Analysis Framework.Diagnostics (Basel, Switzerland) · 2026Article
- Externally validated yet undertrained: sample size deficits in machine learning-based radiomics.European radiology · 2026Article
- AI- and Deep Learning-Based Radiomics: A State-of-the-Art Review.Journal of the Korean Society of Radiology · 2026Review
- The cult of the external validation set: undertraining is only half of the radiomics validation crisis.European radiology · 2026Article
- Magnesium Sulfate in Neonatal Hypoxic-Ischemic Encephalopathy: Bridging the Gap Between Molecular Neuroprotection and Clinical Outcomes.Current issues in molecular biology · 2026Review
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- Do MRI radiomic models truly generalize? External validation of three studies in parotid lesion characterization.European radiology · 2026Article
- Preoperative prediction of parametrial invasion in early-stage cervical cancer: a radiomics nomogram fusing multi-parametric MRI and clinical biomarkers.Abdominal radiology (New York) · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- CT radiomics for survival risk stratification in resectable colorectal liver metastases: a multi-centre study.Scientific reports · 2026Article
- Fusion of Deep Transfer Learning and Radiomics in MRI-Based Prediction of Post-Surgical Recurrence in Soft Tissue Sarcoma.Journal of imaging informatics in medicine · 2026Article
- Residual Conditional Variational Autoencoder for Multi-Center PET/CT Radiomic Feature Harmonization with Integrated Modeling of Batch Effects and Clinical Covariates.Journal of imaging informatics in medicine · 2026Article
- Radiomic analysis of medical imaging for classification in oncology and recommendations for clinically initiated research: a literature review.Quantitative imaging in medicine and surgery · 2026Review
75 more citing papers are in PubMed but not listed here.
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10 authors.
Funding
Abstract
objectiveTest a practical realignment approach to compensate the technical variability of MR radiomic features.
methodsT1 phantom images acquired on 2 scanners, FLAIR and contrast-enhanced T1-weighted (CE-T1w) images of 18 brain tumor patients scanned on both 1.5-T and 3-T scanners, and 36 T2-weighted (T2w) images of prostate cancer patients scanned in one of two centers were investigated. The ComBat procedure was used for harmonizing radiomic features. Differences in statistical distributions in feature values between 1.5- and 3-T images were tested before and after harmonization. The prostate studies were used to determine the impact of harmonization to distinguish between Gleason grades (GGs).
resultsIn the phantom data, 40 out of 42 radiomic feature values were significantly different between the 2 scanners before harmonization and none after. In white matter regions, the statistical distributions of features were significantly different (p < 0.05) between the 1.5- and 3-T images for 37 out of 42 features in both FLAIR and CE-T1w images. After harmonization, no statistically significant differences were observed. In brain tumors, 41 (FLAIR) or 36 (CE-T1w) out of 42 features were significantly different between the 1.5- and 3-T images without harmonization, against 1 (FLAIR) or none (CE-T1w) with harmonization. In prostate studies, 636 radiomic features were significantly different between GGs after harmonization against 461 before. The ability to distinguish between GGs using radiomic features was increased after harmonization.
conclusionComBat harmonization efficiently removes inter-center technical inconsistencies in radiomic feature values and increases the sensitivity of studies using data from several scanners. KEY POINTS: • Radiomic feature values obtained using different MR scanners or imaging protocols can be harmonized by combining off-the-shelf image standardization and feature realignment procedures. • Harmonized radiomic features enable one to pool data from different scanners and centers without a substantial loss of statistical power caused by intra- and inter-center variability. • The proposed realignment method is applicable to radiomic features from different MR sequences and tumor types and does not rely on any phantom acquisition.
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