Evidence map›Paper›PMID 32561069›Full record

ArticleUltrasound in medicine & biology2020

Tumor Vascular Networks Depicted in Contrast-Enhanced Ultrasound Images as a Predictor for Transarterial Chemoembolization Treatment Response.

Ipek Oezdemir, Corrine E Wessner, Colette Shaw, John R Eisenbrey, Kenneth Hoyt

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ultrasound in medicine & biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
3.9field-weighted citation impact, top 5% 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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 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

5 authors at 2 institutions in 1 country.

Ipek OezdemirDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA.
Corrine E WessnerDepartment of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Colette ShawDepartment of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
John R EisenbreyDepartment of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Kenneth HoytDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA. Electronic address: kenneth.hoyt@utdallas.edu.
Thomas Jefferson University · USThe University of Texas at Dallas · US

Funding

2D and 3D Contrast-enhanced Ultrasound Evaluation of HCC ChemoembolizationR01CA194307 · NCI · THOMAS JEFFERSON UNIVERSITY · PI John Eisenbrey · 2016 to 2026
$5.3M
3D Super-Resolution Ultrasound Imaging for Cancer Detection and Treatment MonitoringR01EB025841 · NIBIB · UNIVERSITY OF TEXAS DALLAS · PI HOYT, KENNETH · 2019 to 2022
$1.3M
Molecular Ultrasound Imaging of Cancer Response to Antiangiogenic TherapyK25EB017222 · NIBIB · UNIVERSITY OF TEXAS DALLAS · PI HOYT, KENNETH · 2014 to 2018
$804k
NCI NIH HHS R01 CA194307NIBIB NIH HHS K25 EB017222NIBIB NIH HHS R01 EB025841
6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is prevalent worldwide. Among the various therapeutic options, transarterial chemoembolization (TACE) can be applied to the tumor vascular network by restricting the nutrients and oxygen supply to the tumor. Unique morphologic properties of this network may provide information predictive of future therapeutic responses, which would be significant for decision making during treatment planning. The extraction of morphologic features from the tumor vascular network depicted in abdominal contrast-enhanced ultrasound (CEUS) images faces several challenges, such as organ motion, limited resolution caused by clutter signal and segmentation of the vascular structures at multiple scales. In this study, we present an image processing and analysis approach for the prediction of HCC response to TACE treatment using clinical CEUS images and known pathologic responses. This method focuses on addressing the challenges of CEUS by incorporating a two-stage motion correction strategy, clutter signal removal, vessel enhancement at multiple scales and machine learning for predictive modeling. The morphologic features, namely, number of vessels (NV), number of bifurcations (NB), vessel to tissue ratio (VR), mean vessel length, tortuosity and diameter, from tumor architecture were quantified from CEUS images of 36 HCC patients before TACE treatment. Our analysis revealed that NV, NB and VR are the dominant features for the prediction of long-term TACE response. The model had an accuracy of 86% with a sensitivity and specificity of 89% and 82%, respectively. Reliable prediction of the TACE therapy response using CEUS-derived image features may help to provide personalized therapy planning, which will ultimately improve patient outcomes.

Indexed as

Chemoembolization, TherapeuticContrast MediaAdultAgedCarcinoma, HepatocellularFemaleHumansLiver NeoplasmsMaleMiddle AgedRetrospective StudiesUltrasonographyContrast MediaContrast-enhanced ultrasoundHepatocellular carcinomaMachine learningTransarterial chemoembolizationTumor vascular networks

Identifiers

PMID32561069
PMCPMC7725382
OpenAlexW3034777026

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.