Evidence map›Paper›PMID 36265222›Full record

ArticleEuropean journal of radiology2022

A patient-informed approach to predict iodinated-contrast media enhancement in the liver.

Hananiel Setiawan, Chaofan Chen, Ehsan Abadi, Wanyi Fu, Daniele Marin, Francesco Ria, Ehsan Samei

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

Article in European journal of radiology, 2022. 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
0.1field-weighted citation impact, top 51% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

7 authors at 3 institutions in 1 country.

Hananiel SetiawanCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Center for Virtual Imaging Trials, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Graduate Program in Medical Physics, School of Medicine, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA. Electronic address: hananiel.setiawan@duke.edu.
Chaofan ChenSchool of Computing and Information Science, The University of Maine, 5711 Boardman Hall, Room 348, Orono, ME 04469, USA.
Ehsan AbadiCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Center for Virtual Imaging Trials, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Graduate Program in Medical Physics, School of Medicine, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, 305 Nello L. Teer Engineering Building, Box 90271, Durham, NC 27708, USA; Department of Radiology, School of Medicine, Duke University, Box 3808 DUMC, Durham, NC 27710, USA.
Wanyi FuCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Center for Virtual Imaging Trials, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, 305 Nello L. Teer Engineering Building, Box 90271, Durham, NC 27708, USA.
Daniele MarinGraduate Program in Medical Physics, School of Medicine, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Department of Radiology, School of Medicine, Duke University, Box 3808 DUMC, Durham, NC 27710, USA.
Francesco RiaCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Center for Virtual Imaging Trials, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Clinical Imaging Physics Group, Duke University Health System, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA.
Ehsan SameiCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Center for Virtual Imaging Trials, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Clinical Imaging Physics Group, Duke University Health System, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Graduate Program in Medical Physics, School of Medicine, Duke University, 2424 Erwin Rd, Ste. 302, Durham, NC 27705, USA; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, 305 Nello L. Teer Engineering Building, Box 90271, Durham, NC 27708, USA; Department of Biomedical Engineering, Pratt School of Engineering, Duke University, 305 Nello L. Teer Engineering Building, Box 90271, Durham, NC 27708, USA; Department of Radiology, School of Medicine, Duke University, Box 3808 DUMC, Durham, NC 27710, USA; Physics Building, Science Drive Campus, Box 90305, Durham, NC 27708, USA.
Duke University · USDuke University Health System · USUniversity of Maine · US

Funding

TR&D Project 3: Virtual ReadersP41EB028744 · NIBIB · DUKE UNIVERSITY · PI Ehsan Samei · 2021 to 2026
$7.8M
Simulation Tools for Dynamic CTR01EB001838 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI SAMEI, EHSAN, SEGARS, WILLIAM P · 2005 to 2023
$6.9M
NIBIB NIH HHS P41 EB028744NIBIB NIH HHS R01 EB001838
6 · The paper itself

Abstract

objectiveTo devise a patient-informed time series model that predicts liver contrast enhancement, by integrating clinical data and pharmacokinetics models, and to assess its feasibility to improve enhancement consistency in contrast-enhanced liver CT scans.

methodsThe study included 1577 Chest/Abdomen/Pelvis CT scans, with 70-30% training/validation-testing split. A Gaussian function was used to approximate the early arterial, late arterial, and the portal venous phases of the contrast perfusion curve of each patient using their respective bolus tracking and diagnostic scan data. Machine learning models were built to predict the Gaussian parameters of each patient using the patient attributes (weight, height, age, sex, BMI). Pearson's coefficient, mean absolute error, and root mean squared error were used to assess the prediction accuracy.

resultsThe integration of the pharmacokinetics model with a two-layered neural network achieved the highest prediction accuracy on the test data (R

conclusionsA new model using a Gaussian function and supervised machine learning can be used to build liver parenchyma contrast enhancement prediction model. The model can have utility in clinical settings to optimize and improve consistency in contrast-enhanced liver imaging.

Indexed as

Contrast MediaLiver NeoplasmsAbdomenHumansLiverTomography, X-Ray ComputedContrast Mediacontrast CTcontrast-enhanced CTContrast perfusionIodinated contrast enhancementLiver enhancement

Identifiers

PMID36265222
PMCPMC10777297
OpenAlexW4304944612

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.