Trial reportJournal of magnetic resonance imaging : JMRI2015
Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial.
Trial report in Journal of magnetic resonance imaging : JMRI, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01564368 (Diffusion Weighted MR Imaging Biomarkers for Assessment of Breast Cancer Response to Neoadjuvant Treatment), which is not on this map. Cited by 37 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Diffusion Weighted MR Imaging Biomarkers for Assessment of Breast Cancer Response to Neoadjuvant Treatment: A Sub-study of the I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And MoLecular Analysis)
Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper.Frontiers in oncology · 2021Pooled it
- Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group.European radiology · 2020Guideline
- An Open-Source, Reproducible MRI Data Acquisition and Reconstruction Workflow Using Pulseq: Multi-Site and Cross-Vendor Validation.Magnetic resonance in medicine · 2026Article
- On-Scanner Correction of Gradient Nonlinearity Bias for Accurate Assessment of Diffusion Heterogeneity Across Bone Sites in Myelofibrosis Patients.Magnetic resonance in medicine · 2026Article
- Is correction for gradient nonlinearity necessary in a brain diffusion tensor MRI clinical study?PloS one · 2026Article
- MRI acquisition and reconstruction cookbook: recipes for reproducibility, served with real-world flavour.Magma (New York, N.Y.) · 2025Review
- Simultaneous coherent-incoherent motion imaging in brain parenchyma.Interface focus · 2025Article
- Feasibility of brain intra-axonal microstructure imaging with ultrahigh B-encoding using MAGNUS ultra-high-performance gradients.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Histogram analysis comparison of readout-segmented and single-shot echo-planar imaging for differentiating luminal from non-luminal breast cancer.Scientific reports · 2024Article
- Efficient approximate signal reconstruction for correction of gradient nonlinearities in diffusion-weighted imaging.Magnetic resonance imaging · 2023Article
- Article
- Mapping the impact of nonlinear gradient fields with noise on diffusion MRI.Magnetic resonance imaging · 2023Article
- Mapping the Impact of Approximate Gradient Nonlinearity Fields Correction on Tractography.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Contrastive semi-supervised harmonization of single-shell to multi-shell diffusion MRI.Magnetic resonance imaging · 2022Article
- Technical note: Temperature and concentration dependence of water diffusion in polyvinylpyrrolidone solutions.Medical physics · 2022Article
- Long-Term Stability of Gradient Characteristics Warrants Model-Based Correction of Diffusion Weighting Bias.Tomography (Ann Arbor, Mich.) · 2022Article
- Factors affecting the value of diffusion-weighted imaging for identifying breast cancer patients with pathological complete response on neoadjuvant systemic therapy: a systematic review.Insights into imaging · 2021Review
- Empirical validation of gradient field models for an accurate ADC measured on clinical 3T MR systems in body oncologic applications.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Article
- Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI.Magnetic resonance imaging · 2021Article
- A comparative study of methods for determining Intravoxel incoherent motion parameters in cervix cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo evaluate a gradient nonlinearity correction (GNC) program for quantitative apparent diffusion coefficient (ADC) measurements on phantom and human subject diffusion-weighted (DW) magnetic resonance imaging (MRI) scans in a multicenter breast cancer treatment response study MATERIALS AND
methodsA GNC program using fifth-order spherical harmonics for gradient modeling was applied retrospectively to qualification phantom and human subject scans. Ice-water phantoms of known diffusion coefficient were scanned at five different study centers with different scanners and receiver coils. Human in vivo data consisted of baseline and early-treatment exams on 54 patients from four sites. ADC maps were generated with and without GNC. Regions of interest were defined to quantify absolute errors and changes with GNC over breast imaging positions.
resultsPhantom ADC errors varied with region of interest (ROI) position and scanner configuration; the mean error by configuration ranged from 1.4% to 19.9%. GNC significantly reduced the overall mean error for all sites from 9.9% to 0.6% (P = 0.016). Spatial dependence of GNC was highest in the right-left (RL) and anterior-posterior (AP) directions. Human subject mean tumor ADC was reduced 0.2 to 12% by GNC at different sites. By regression, every 1-cm change in tumor ROI position between baseline and follow-up visits resulted in an estimated change of 2.4% in the ADC early-treatment response measurement.
conclusionGNC is effective for removing large, system-dependent errors in quantitative breast DWI. GNC may be important in ensuring reproducibility in multicenter studies and in reducing errors in longitudinal treatment response measures arising from spatial variations in tumor position between visits.
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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 OpenQuestion graph.