ArticleMagma (New York, N.Y.)2021
A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study.
Article in Magma (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Multi-institutional MRI-based radiomic pilot study to measure the variations between scanner vendors and imaging sessions.Frontiers in oncology · 2026Article
- Prediction of Breast Cancer Response to Neoadjuvant Therapy with Machine Learning: A Clinical, MRI-Qualitative, and Radiomics Approach.Life (Basel, Switzerland) · 2025Article
- Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - How are they affected by variations in tumor delineation?Computational and structural biotechnology journal · 2024Article
- Mitigating Interobserver Variability in Radiomics with ComBat: A Feasibility Study.Journal of imaging · 2024Article
- Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: a phantom study.Medical & biological engineering & computing · 2024Article
- The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers.BMC medical imaging · 2024Article
- Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy.European radiology · 2023Observational
- ComBat Harmonization of Myocardial Radiomic Features Sensitive to Cardiac MRI Acquisition Parameters.Radiology. Cardiothoracic imaging · 2023Article
- Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.NeuroImage · 2023Review
- Article
- Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation.Diagnostics (Basel, Switzerland) · 2023Article
- Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI.European radiology · 2023Article
- Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities.Frontiers in medicine · 2023Article
- Harmonization Strategies in Multicenter MRI-Based Radiomics.Journal of imaging · 2022Review
- Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.Journal of magnetic resonance imaging : JMRI · 2022Article
- Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1.Scientific data · 2022Article
- Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.An international journal on information fusion · 2022Article
- Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features.Cancers · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
objectiveQuantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies. MATERIALS AND
methodsT1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using Z score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions.
resultsIntra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms. DISCUSSION: A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.
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