ArticlePloS one2021
A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.European journal of nuclear medicine and molecular imaging · 2023Guideline
- Undertraining is the floor, not the ceiling: when statistical and validation failures compound in radiomics.European radiology · 2026Article
- Development and external validation of a FDG PET-based radiomics model predicting occult lymph node metastasis in non-small cell lung cancer patients.European journal of nuclear medicine and molecular imaging · 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
- Evaluating Transferability of ComBat Harmonization of Diffusion Tensor Magnetic Resonance Imaging Data.Annals of biomedical engineering · 2026Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes.EJNMMI physics · 2025Article
- A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.Artificial intelligence in medicine · 2025Article
- Long-axial field-of-view PET/CT improves radiomics feature reliability.European journal of nuclear medicine and molecular imaging · 2025Article
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Multi-cohort, multi-sequence harmonisation for cerebrovascular brain age.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort.Frontiers in oncology · 2025Article
- A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.bioRxiv : the preprint server for biology · 2024Article
- The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers.BMC medical imaging · 2024Article
- Pre-treatment T2-weighted magnetic resonance radiomics for prediction of loco-regional recurrence after image-guided adaptive brachytherapy for locally advanced cervical cancer.Journal of contemporary brachytherapy · 2024Article
- Modeling the impact of MRI acquisition bias on structural connectomes: Harmonizing structural connectomes.Network neuroscience (Cambridge, Mass.) · 2024Article
- Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data.Nuklearmedizin. Nuclear medicine · 2023Review
- MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features.Diagnostics (Basel, Switzerland) · 2023Article
- Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.NeuroImage · 2023Review
- Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization.Physics and imaging in radiation oncology · 2023Article
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability in scanner models, acquisition protocols and reconstruction settings, which is often unavoidable in a multicentre retrospective analysis. A statistical harmonization strategy called ComBat was utilized in radiomics studies to deal with the "center-effect". The goal of the present work was to integrate a transfer learning (TL) technique within ComBat-and recently developed alternate versions of ComBat with improved flexibility (M-ComBat) and robustness (B-ComBat)-to allow the use of a previously determined harmonization transform to the radiomic feature values of new patients from an already known center. MATERIAL AND
methodsThe proposed TL approach were incorporated in the four versions of ComBat (standard, B, M, and B-M ComBat). The proposed approach was evaluated using a dataset of 189 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging (MRI) and positron emission tomography (PET) images, with the clinical endpoint of predicting local failure. The impact performance of the TL approach was evaluated by comparing the harmonization achieved using only parts of the data to the reference (harmonization achieved using all the available data). It was performed through three different machine learning pipelines.
resultsThe proposed TL technique was successful in harmonizing features of new patients from a known center in all versions of ComBat, leading to predictive models reaching similar performance as the ones developed using the features harmonized with all the data available.
conclusionThe proposed TL approach enables applying a previously determined ComBat transform to new, previously unseen data.
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