Evidence map›Paper›PMID 31549327›Full record

ArticleAnnals of biomedical engineering2020

High Frequency Spectral Ultrasound Imaging to Detect Metastasis in Implanted Biomaterial Scaffolds.

Grace G Bushnell, Xiaowei Hong, Rachel M Hartfield, Yining Zhang, Robert S Oakes, Shreyas S Rao, Jacqueline S Jeruss, Jan P Stegemann, Cheri X Deng, Lonnie D Shea

Open access · greenAbstract read
In one paragraph

Article in Annals of biomedical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 28% of its field
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

6 citing papers in PubMed, 13 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Grace G BushnellDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Xiaowei HongDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Rachel M HartfieldDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Yining ZhangDepartment of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Robert S OakesDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Shreyas S RaoDepartment of Chemical and Biological Engineering, University of Alabama, Tuscaloosa, AL, 35487, USA.
Jacqueline S JerussDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Jan P StegemannDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Cheri X DengDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA. cxdeng@umich.edu.
Lonnie D SheaDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA. ldshea@umich.edu.
University of Michigan–Ann Arbor · USUniversity of Alabama · US

Funding

An in vivo metastasis sensorR01CA173745 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BACKMAN, VADIM, SHEA, LONNIE D · 2012 to 2016
$4.2M
Measuring Signaling Pathway Dynamics During Tissue Growth in HydrogelsR01CA214384 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI JERUSS, JACQUELINE SARA, SHEA, LONNIE D · 2017 to 2021
$1.8M
Microscale Mechanobiology for Musculoskeletal Tissue Engineering using Advanced Ultrasound TechniquesR01DE026630 · NIDCR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DENG, CHERI X, STEGEMANN, JAN P. · 2017 to 2021
$1.8M
Bone Regeneration Using Osteogenic and Vasculogenic Tissue ModulesR01AR062636 · NIAMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI STEGEMANN, JAN P. · 2014 to 2018
$1.7M
Heterogeneity of early metastatic tumor cellsF31CA224982 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BUSHNELL, GRACE GILMORE · 2018 to 2019
$56k
NCI NIH HHS F31 CA224982NCI NIH HHS F31 CA224982-01NCI NIH HHS R01 CA173745NCI NIH HHS R01CA173745NCI NIH HHS R01 CA214384NCI NIH HHS R01CA214384NIAMS NIH HHS R01 AR062636NIDCR NIH HHS R01 DE026630NIDCR NIH HHS R01DE026630
6 · The paper itself

Abstract

For most cancers, metastasis is the point at which disease is no longer curable. Earlier detection of metastasis, when it is undetectable by current clinical methods, may enable better outcomes. We have developed a biomaterial implant that recruits metastatic cancer cells in mouse models of breast cancer. Here, we investigate spectral ultrasound imaging (SUSI) as a non-invasive strategy for detecting metastasis to the implanted biomaterial scaffolds. Our results show that SUSI, which detects parameters related to tissue composition and structure, identified changes at an early time point when tumor cells were recruited to scaffolds in orthotopic breast cancer mouse models. These changes were not associated with acellular components in the scaffolds but were reflected in the cellular composition in the scaffold microenvironment, including an increase in CD31 + CD45-endothelial cell number in tumor bearing mice. In addition, we built a classification model based on changes in SUSI parameters from scaffold measurements to stratify tumor free and tumor bearing status. Combination of a linear discriminant analysis and bagged decision trees model resulted in an area under the curve of 0.92 for receiver operating characteristics analysis. With the potential for early non-invasive detection, SUSI could facilitate clinical translation of the scaffolds for monitoring metastatic disease.

Indexed as

Tissue ScaffoldsAnimalsBiocompatible MaterialsBreast NeoplasmsCell Line, TumorFemaleHumansMiceUltrasonographyBiocompatible MaterialsCancerCancer diagnosticsMetastasis detectionPre-metastatic nicheSpectral ultrasound imaging

Identifiers

PMID31549327
PMCPMC6930322
OpenAlexW2975533350

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.