Evidence map›Paper›PMID 34748928›Full record

ArticleMagnetic resonance imaging2022

Quantitative transport mapping (QTM) for differentiating benign and malignant breast lesion: Comparison with traditional kinetics modeling and semi-quantitative enhancement curve characteristics.

Qihao Zhang, Pascal Spincemaille, Michele Drotman, Christine Chen, Sarah Eskreis-Winkler, Weiyuan Huang, Liangdong Zhou, John Morgan, Thanh D Nguyen, Martin R Prince and 1 more

Abstract read
In one paragraph

Article in Magnetic resonance imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

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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

11 authors.

Qihao ZhangDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America; Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States of America.
Pascal SpincemailleDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America.
Michele DrotmanDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America.
Christine ChenDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America.
Sarah Eskreis-WinklerDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America.
Weiyuan HuangDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America.
Liangdong ZhouDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America.
John MorganDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America.
Thanh D NguyenDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America.
Martin R PrinceDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America.
Yi WangDepartment of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America; Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States of America. Electronic address: yw233@cornell.edu.

Funding

3.0T Magnetic Resonance Imaging System for Preclinical, Translational and Clinical ResearchS10OD021782 · OD · WEILL MEDICAL COLL OF CORNELL UNIV · PI WANG, YI · 2016 to 2016
$2.0M
NIH HHS S10 OD021782
6 · The paper itself

Abstract

purposeTo test the feasibility of using quantitative transport mapping (QTM) method, which is based on the inversion of transport equation using spatial deconvolution without any arterial input function, for automatically postprocessing dynamic contrast enhanced MRI (DCE-MRI) to differentiate malignant and benign breast tumors. MATERIALS AND

methodsBreast DCE-MRI data with biopsy confirmed malignant (n = 13) and benign tumors (n = 13) was used to assess QTM velocity (|u|) and diffusion coefficient (D), volume transfer constant (K

resultsBetween malignant and benign tumors, there was a significant difference in |u| and K

conclusionQTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with traditional kinetics method and ECC, QTM method showed better diagnostic accuracy in differentiating benign from malignant breast tumors in this study.

Indexed as

Breast NeoplasmsContrast MediaBreastDiagnosis, DifferentialDiffusion Magnetic Resonance ImagingFemaleHumansKineticsMagnetic Resonance ImagingRetrospective StudiesROC CurveContrast MediaArterial input function (AIF)Breast tumor diagnosisDynamic contrast enhanced (DCE) MRIFluid flow velocityQuantitative Transport Mapping (QTM)

Identifiers

PMID34748928
PMCPMC8726426

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Registered trials

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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.