ArticleThe British journal of radiology2020
An automated approach for the optimised estimation of breast density with Dixon methods.
Article in The British journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Relevance of advanced imaging analysis units in radiology departments: a narrative review.International journal of computer assisted radiology and surgery · 2026Review
- Quantitative background parenchymal enhancement and fibro-glandular density at breast MRI: Association with BRCA status.European radiology · 2023Article
- Volumetric breast density estimation on MRI using explainable deep learning regression.Scientific reports · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo present and evaluate an automated method to correct scaling between Dixon water/fat images used in breast density (BD) assessments.
methodsDixon images were acquired in 14 subjects with different
resultsCorrected/uncorrected BD ranged from, respectively, 10.9-71.8%/8.9-66.7% for low-FA data to 8.1-74.3%/5.6-54.3% for high-FA data. Corrected metrics had an average absolute increase in BD of 6.4% for low-FA data and 18.4% for high-FA data. BD values estimated from low- and high-FA data were closer following SF-correction.
conclusionOur results demonstrate need for scaling in such BD assessments, where our method brought high-FA and low-FA data into closer agreement. ADVANCES IN KNOWLEDGE: We demonstrated a feasible method to address a main source of inaccuracy in Dixon-based BD measurements.
Indexed as
Identifiers
What OpenQuestion holds
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.