ArticleScientific data2024
Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 55 citations in OpenAlex.
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- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- HARMONY: A large-scale harmonized neuroimaging dataset for research on anxious misery disorders.bioRxiv : the preprint server for biology · 2026Article
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- 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
- Structural brain imaging biomarkers for predicting seizure recurrence after a first unprovoked seizure.Epilepsia open · 2026Article
- A precision medicine trial of bupropion and sertraline for major depressive disorder using a biomarker-guided sequential multiple-assignment design.Nature. Mental health · 2026Article
- Ethical and Regulatory Frameworks for Artificial Intelligence in Clinical Research: A European Perspective on the Artificial Intelligence Act for Ethics Committees and Researchers.European cardiology · 2026Review
- Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification.Insights into imaging · 2025Article
- Challenges and best practices when using ComBAT to harmonize diffusion MRI data.Scientific reports · 2025Article
- Impact of image preprocessing methods on MRI radiomics feature variability and classification performance in Parkinson's disease motor subtype analysis.Scientific reports · 2025Article
- Unlocking the potential of radiomics in identifying fibrosing and inflammatory patterns in interstitial lung disease.La Radiologia medica · 2025Article
- Overcoming Site Variability in Multisite fMRI Studies: an Autoencoder Framework for Enhanced Generalizability of Machine Learning Models.Neuroinformatics · 2025Article
- Current challenges and future directions for brain age prediction in children and adolescents.Nature communications · 2025Review
- Age- and Sex-Specific Cerebral Blood Flow Atlases for Healthy Brain Across the Lifespan.Scientific data · 2025Article
- Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI.Scientific data · 2025Article
- Artificial Intelligence Is Brittle: We Need to Do Better.Radiology. Artificial intelligence · 2025Article
- HeteroMRI: Robust white matter abnormality classification across multi-scanner MRI data.GigaScience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.