Evidence map›Paper›PMID 39641987›Full record

ArticleMagnetic resonance in medicine2025

Estimation of fatty acid composition in mammary adipose tissue using deep neural network with unsupervised training.

Suneeta Chaudhary, Elizabeth G Lane, Allison Levy, Anika McGrath, Eralda Mema, Melissa Reichmann, Katerina Dodelzon, Katherine Simon, Eileen Chang, Marcel Dominik Nickel and 3 more

Abstract read
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In one paragraph

Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Suneeta ChaudharyDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.ORCID https://orcid.org/0000-0003-0170-909X
Elizabeth G LaneDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Allison LevyDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Anika McGrathDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Eralda MemaDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Melissa ReichmannDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Katerina DodelzonDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Katherine SimonDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Eileen ChangDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Marcel Dominik NickelMR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany.
Linda MoyDepartment of Radiology, New York University School of Medicine, New York, New York, USA.
Michele DrotmanDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Sungheon Gene KimDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.ORCID https://orcid.org/0000-0002-6288-0678

Funding

DCE MRI Study for Breast Cancer.R01CA160620 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI KIM, SUNGHEON GENE · 2012 to 2023
$5.4M
Gradient-Echo Spectroscopic Imaging Study of Saturated Fat and Breast CancerR01CA219964 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI KIM, SUNGHEON GENE, MOY, LINDA · 2018 to 2022
$2.3M
Diffusion MRI of Treatment Response for De-escalation of Radiation TherapyUH3CA228699 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI KIM, SUNGHEON GENE · 2021 to 2023
$1.2M
NCI NIH HHS R01 CA160620NCI NIH HHS R01CA160620NCI NIH HHS R01 CA219964NCI NIH HHS R01CA219964NCI NIH HHS UH3 CA228699NCI NIH HHS UH3CA228699
6 · The paper itself

Abstract

purposeTo develop a deep learning-based method for robust and rapid estimation of the fatty acid composition (FAC) in mammary adipose tissue.

methodsA physics-based unsupervised deep learning network for estimation of fatty acid composition-network (FAC-Net) is proposed to estimate the number of double bonds and number of methylene-interrupted double bonds from multi-echo bipolar gradient-echo data, which are subsequently converted to saturated, mono-unsaturated, and poly-unsaturated fatty acids. The loss function was based on a 10 fat peak signal model. The proposed network was tested with a phantom containing eight oils with different FAC and on post-menopausal women scanned using a whole-body 3T MRI system between February 2022 and January 2024. The post-menopausal women included a control group (n = 8) with average risk for breast cancer and a cancer group (n = 7) with biopsy-proven breast cancer.

resultsThe FAC values of eight oils in the phantom showed strong correlations between the measured and reference values (R

conclusionThe results in this study suggest that the proposed FAC-Net can be used to measure the FAC of mammary adipose tissue from gradient-echo MRI data of the breast.

Indexed as

Adipose TissueBreastDeep LearningFatty AcidsMagnetic Resonance ImagingUnsupervised Machine LearningBreast NeoplasmsFemaleHumansImage Processing, Computer-AssistedMiddle AgedNeural Networks, ComputerPhantoms, ImagingFatty Acidsbreast cancerdeep neural networkfatty acid compositionmammary adipose tissue

Identifiers

PMID39641987
PMCPMC11893257

What Socratic holds

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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 Socratic graph.