Evidence map›Paper›PMID 38463607›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2024

Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom.

Chengyue Wu, David A Hormuth, Ty Easley, Federico Pineda, Gregory S Karczmar, Thomas E Yankeelov

Abstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

6 authors.

Chengyue WuUniversity of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States.ORCID https://orcid.org/0000-0002-3789-404X
David A HormuthUniversity of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States.ORCID https://orcid.org/0000-0002-9643-1694
Ty EasleyWashington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States.ORCID https://orcid.org/0000-0002-8215-2598
Federico PinedaUniversity of Chicago, Department of Radiology, Chicago, Illinois, United States.ORCID https://orcid.org/0000-0002-9073-4379
Gregory S KarczmarUniversity of Chicago, Department of Radiology, Chicago, Illinois, United States.
Thomas E YankeelovUniversity of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States.ORCID https://orcid.org/0000-0001-6201-3913

Funding

VIRAL ONCOLOGY CORE FACILITYP30CA014599 · NCI · UNIVERSITY OF CHICAGO · PI KUNLE ODUNSI · 1985 to 2026
$122.1M
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
Breast Cancer Screening with Quantitative Ultra-Fast DCEMRI and Clinical Risk AssessmentR01CA218700 · NCI · UNIVERSITY OF CHICAGO · PI KARCZMAR, GREGORY S. · 2017 to 2021
$3.0M
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
Quantitative DCEMRI of Prostate Cancer Correlation with Gold StandardsR01CA172801 · NCI · UNIVERSITY OF CHICAGO · PI KARCZMAR, GREGORY S., OTO, AYTEKIN · 2013 to 2017
$2.3M
NCI NIH HHS P30 CA014599NCI NIH HHS R01 CA172801NCI NIH HHS R01 CA218700NCI NIH HHS U01 CA142565NCI NIH HHS U01 CA174706NCI NIH HHS U24 CA226110
6 · The paper itself

Abstract

Purpose: Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Approach: A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to Results: The observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization. Conclusion: An

Indexed as

acquisition optimizationfluid mechanicsin silico validationquantitative DCE-MRI

Identifiers

PMID38463607
PMCPMC10921778

What OpenQuestion holds

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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.