Evidence map›Paper›PMID 41553443›Full record

ArticleEuropean radiology experimental2026

Comparison of postprocessing metrics in multimetabolic APT-weighted CEST and 2-deoxy-D-glucose-CEST-MRI for differentiating breast cancer subtypes in a murine model.

Daniela Prinz, Silvester J Bartsch, Joachim Friske, Martin Krššák, Daniela Laimer-Gruber, Thomas H Helbich, Katja Pinker

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European radiology experimental, 2026. 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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1 · What the graph read from it

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Daniela PrinzDivision of Molecular and Structural Preclinical Imaging (PIL), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Silvester J BartschDivision of Molecular and Structural Preclinical Imaging (PIL), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Joachim FriskeDivision of Molecular and Structural Preclinical Imaging (PIL), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Martin KrššákDivision of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna, Austria.
Daniela Laimer-GruberDivision of Molecular and Structural Preclinical Imaging (PIL), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Thomas H HelbichDivision of Molecular and Structural Preclinical Imaging (PIL), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Katja PinkerDivision of Molecular and Structural Preclinical Imaging (PIL), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria. kp3124@cumc.columbia.edu.ORCID http://orcid.org/0000-0002-2722-7331

Funding

Vienna Science and Technology Fund LS19-018
6 · The paper itself

Abstract

backgroundChemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI), particularly amide proton transfer-weighted (APTw)-CEST and 2-deoxy-D-glucose-CEST, holds promise for noninvasive molecular breast cancer (BC) characterization. However, quantification remains challenging due to field inhomogeneities, overlapping exchange pools, and the limited robustness of conventional metrics such as the magnetization transfer ratio asymmetry (MTR MATERIALS AND

methodsMetabolic CEST-MRI was performed in vitro on protein and 2-deoxy-D-glucose phantoms and in vivo in a murine BC model. Imaging was conducted at 9.4 T with 120 frequency offsets from +6 to -6 ppm. MTR

resultsIn APTw-CEST, MTR

conclusionAdvanced metrics, particularly MTR RELEVANCE STATEMENT: Our findings underscore the importance of applying advanced postprocessing metrics to metabolic CEST-MRI for improved noninvasive BC characterization in a murine preclinical model. KEY POINTS: Advanced multimetabolic APTw-CEST and 2-deoxy-D-glucose-CEST postprocessing metrics allowed adequate preclinical murine BC subtyping. AREX showed potential for 2-deoxy-D-glucose-CEST in tumor characterization; however, APTw-CEST remains superior. MTR

Indexed as

Breast NeoplasmsDeoxyglucoseMagnetic Resonance ImagingAnimalsCell Line, TumorDiagnosis, DifferentialDisease Models, AnimalFemaleHumansMiceDeoxyglucoseBreast neoplasmsDisease models (animal)Magnetic resonance imagingMiceMolecular imaging

Identifiers

PMID41553443
PMCPMC12816453

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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 OpenQuestion graph.