Dataset Information

Reproducible automated breast density measure with no ionizing radiation using fat-water decomposition MRI.

ABSTRACT: BACKGROUND:Increased breast density is a significant independent risk factor for breast cancer, and recent studies show that this risk is modifiable. Hence, breast density measures sensitive to small changes are desired. PURPOSE:Utilizing fat-water decomposition MRI, we propose an automated, reproducible breast density measurement, which is nonionizing and directly comparable to mammographic density (MD). STUDY TYPE:Retrospective study. POPULATION:The study included two sample sets of breast cancer patients enrolled in a clinical trial, for concordance analysis with MD (40 patients) and reproducibility analysis (10 patients). FIELD STRENGTH/SEQUENCE:The majority of MRI scans (59 scans) were performed with a 1.5T GE Signa scanner using radial IDEAL-GRASE sequence, while the remaining (seven scans) were performed with a 3T Siemens Skyra using 3D Cartesian 6-echo GRE sequence with a similar fat-water separation technique. ASSESSMENT:After automated breast segmentation, breast density was calculated using FraGW, a new measure developed to reliably reflect the amount of fibroglandular tissue and total water content in the entire breast. Based on its concordance with MD, FraGW was calibrated to MR-based breast density (MRD) to be comparable to MD. A previous breast density measurement, Fra80-the ratio of breast voxels with <80% fat fraction-was also calculated for comparison with FraGW. STATISTICAL TESTS:Pearson correlation was performed between MD (reference standard) and FraGW (and Fra80). Test-retest reproducibility of MRD was evaluated using the difference between test-retest measures (?1-2 ) and intraclass correlation coefficient (ICC). RESULTS:Both FraGW and Fra80 were strongly correlated with MD (Pearson ?: 0.96 vs. 0.90, both P?

SUBMITTER: Ding J

PROVIDER: S-EPMC6173993 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Reproducible automated breast density measure with no ionizing radiation using fat-water decomposition MRI.

Ding Jie J Stopeck Alison T AT Gao Yi Y Marron Marilyn T MT Wertheim Betsy C BC Altbach Maria I MI Galons Jean-Philippe JP Roe Denise J DJ Wang Fang F Maskarinec Gertraud G Thomson Cynthia A CA Thompson Patricia A PA Huang Chuan C

Journal of magnetic resonance imaging : JMRI 20180406 4

<h4>Background</h4>Increased breast density is a significant independent risk factor for breast cancer, and recent studies show that this risk is modifiable. Hence, breast density measures sensitive to small changes are desired.<h4>Purpose</h4>Utilizing fat-water decomposition MRI, we propose an automated, reproducible breast density measurement, which is nonionizing and directly comparable to mammographic density (MD).<h4>Study type</h4>Retrospective study.<h4>Population</h4>The study included ...[more]

PMID: 29630755

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Project description:The recently developed magnetic resonance imaging (MRI) proton density fat fraction (PDFF) allows measurement of the fat in all segments of hepatic tissue. However, it is time consuming and inconvenient to measure each segment repeatedly. Moreover, volume of each segment also should be adjusted with arithmetic mean of the selected segments when total amount of liver fat is estimated. Therefore, we try to develop a clinically-relevant and applicable method of estimating hepatic fat in PDFF image.A total of 164 adults were enrolled. We addressed the measurement frequency and segment selection to determine the optimal method of measuring intrahepatic fat. Total hepatic fat was estimated by the weighted mean of each segment reflecting their respective segmental volumes. We designed 2 models. In Model 1, we determined the segment order by which the mean was closest to the whole weighted mean. In Model 2, we determined the segment order by which the arithmetic mean of the selected segments was closest to the whole weighted mean.Fat fraction (FF) was most important risk factor of hepatic heterogeneity in multivariable analysis (β = 0.534, P < .001). In severe fatty liver (FF > 22.1%), intrahepatic fat variability was 2.47% (1.16-6.26%). The arithmetic mean total intrahepatic FF was 12.66%. But the weighted mean that applied to each segmental volume was 12.90%. In Model 1, arithmetic mean of segments 4 and 5 was closest to the total estimated hepatic fat amount. However, when we added segment 8, the mean of segments 4, 5, and 8 was significantly different from the estimated total hepatic fat amount (P = .0021). In Model 2, arithmetic mean of segments 4 and 5 was closest to the total estimated hepatic fat amount. There was a significant reduction in variability between segment 4 and segments 4 and 5 (P < .0001).Averaging the mean hepatic FF of segments 4 and 5 was the most reasonable method for estimating total intrahepatic fat in practice.

Project description:Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps.Supplementary informationThe online version contains supplementary material available at 10.1186/s40537-022-00677-1.

Project description:BackgroundWhole genome duplication (WGD) events are common in the evolutionary history of many living organisms. For decades, researchers have been trying to understand the genetic and epigenetic impact of WGD and its underlying molecular mechanisms. Particular attention was given to allopolyploid study systems, species resulting from an hybridization event accompanied by WGD. Investigating the mechanisms behind the survival of a newly formed allopolyploid highlighted the key role of DNA methylation. With the improvement of high-throughput methods, such as whole genome bisulfite sequencing (WGBS), an opportunity opened to further understand the role of DNA methylation at a larger scale and higher resolution. However, only a few studies have applied WGBS to allopolyploids, which might be due to lack of genomic resources combined with a burdensome data analysis process. To overcome these problems, we developed the Automated Reproducible Polyploid EpiGenetic GuIdance workflOw (ARPEGGIO): the first workflow for the analysis of epigenetic data in polyploids. This workflow analyzes WGBS data from allopolyploid species via the genome assemblies of the allopolyploid's parent species. ARPEGGIO utilizes an updated read classification algorithm (EAGLE-RC), to tackle the challenge of sequence similarity amongst parental genomes. ARPEGGIO offers automation, but more importantly, a complete set of analyses including spot checks starting from raw WGBS data: quality checks, trimming, alignment, methylation extraction, statistical analyses and downstream analyses. A full run of ARPEGGIO outputs a list of genes showing differential methylation. ARPEGGIO was made simple to set up, run and interpret, and its implementation ensures reproducibility by including both package management and containerization.ResultsWe evaluated ARPEGGIO in two ways. First, we tested EAGLE-RC's performance with publicly available datasets given a ground truth, and we show that EAGLE-RC decreases the error rate by 3 to 4 times compared to standard approaches. Second, using the same initial dataset, we show agreement between ARPEGGIO's output and published results. Compared to other similar workflows, ARPEGGIO is the only one supporting polyploid data.ConclusionsThe goal of ARPEGGIO is to promote, support and improve polyploid research with a reproducible and automated set of analyses in a convenient implementation. ARPEGGIO is available at https://github.com/supermaxiste/ARPEGGIO .

Dataset Information

Reproducible automated breast density measure with no ionizing radiation using fat-water decomposition MRI.

Publications

Reproducible automated breast density measure with no ionizing radiation using fat-water decomposition MRI.

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