Evidence map›Paper›PMID 36321574›Full record

ArticleMagnetic resonance in medicine2023

Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast.

Julie C DiCarlo, Angela M Jarrett, Anum S Kazerouni, John Virostko, Anna Sorace, Kalina P Slavkova, Stefanie Woodard, Sarah Avery, Debra Patt, Boone Goodgame and 1 more

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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.

Julie C DiCarloThe Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA.ORCID 0000-0002-6965-3611
Angela M JarrettThe Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA.
Anum S KazerouniDepartment of Radiology, University of Washington, Seattle, Washington, USA.ORCID 0000-0002-4200-534X
John VirostkoThe Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA.
Anna SoraceDepartment of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Kalina P SlavkovaDepartment of Physics, The University of Texas at Austin, Austin, Texas, USA.ORCID 0000-0003-4311-2355
Stefanie WoodardDepartment of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Sarah AveryDepartment of Diagnostic Medicine, The University of Texas at Austin, Austin, Texas, USA.
Debra PattTexas Oncology, Austin, Texas, USA.
Boone GoodgameDepartment of Oncology, University of Texas at Austin, Austin, Texas, USA.
Thomas E YankeelovThe Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA.

Funding

Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant TherapyU01CA142565 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI KARCZMAR, GREGORY S., NANDA, RITA · 2010 to 2019
$4.5M
INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCERU24CA226110 · NCI · BAYLOR COLLEGE OF MEDICINE · PI LEWIS, MICHAEL T., RUBIN, DANIEL L · 2019 to 2023
$3.2M
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast CancerU01CA174706 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, YANKEELOV, THOMAS E · 2013 to 2018
$2.5M
NCI NIH HHS U01 CA142565NCI NIH HHS U01 CA174706NCI NIH HHS U24 CA226110
6 · The paper itself

Abstract

purposeA method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition. THEORY: Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters K

methodsThe DCE-MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety-Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest-area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points.

resultsThe optimal three-point sample times were from the data-informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor-median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for K

conclusionIt is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE-MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high-spatial-resolution DCE-MRI images.

Indexed as

BreastBreast NeoplasmsContrast MediaFemaleHumansMagnetic Resonance ImagingRetrospective StudiesContrast Mediabreast cancerperfusionpharmacokinetic modelingsampling

Identifiers

PMID36321574
PMCPMC9792438

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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.