Evidence map›Paper›PMID 30529712›Full record

ArticleComputers in biology and medicine2019

A two-dimensional (2D) systems biology-based discrete liver tissue model: A simulation study with implications for ultrasound elastography of liver fibrosis.

Yu Wang, Jingfeng Jiang

Abstract read
In one paragraph

Article in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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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

2 authors.

Yu WangDepartment of Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA.
Jingfeng JiangDepartment of Biomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA; Department of Mechanical Engineering and Engineering Mechanics, Michigan Technological University, Houghton, MI, 49931, USA. Electronic address: jjiang1@mtu.edu.

Funding

Virtual Breast Project: Improving Noninvasive Characterization of TumorsR15CA179409 · NCI · MICHIGAN TECHNOLOGICAL UNIVERSITY · PI JIANG, JINGFENG · 2014 to 2014
$453k
Elastography-based Analytics for Benign and Malignant Breast DiseaseR15EB026197 · NIBIB · MICHIGAN TECHNOLOGICAL UNIVERSITY · PI JIANG, JINGFENG · 2018 to 2018
$450k
NCI NIH HHS R15 CA179409NIBIB NIH HHS R15 EB026197
6 · The paper itself

Abstract

Continuum tissue models that were often used to simulate or analyze the mechanical properties of tissues being imaged may not be biologically realistic. Our primary objective was to establish the feasibility of using systems biology to construct biologically relevant tissue models linking tissue structure, composition and architecture to the ultrasound measurements directly. The first application was designated to model fibrotic liver tissues. The proposed liver tissue model leveraged established histopathology knowledge of fibrotic liver tissues. Particularly, rules of systems biology derived from molecular histopathology were first implemented into an agent-based software platform SPARK to reflect progressions of liver fibrosis with/without steatosis. Then, microscopic compositions of tissues (e.g. cellular components) were converted to computing grids (at the 50-100 μm scale) for wave simulations using an open-source K-Wave. To verify the physical soundness of the proposed model, virtual wave speed measurements (i.e. shear wave speed [SWS] and the speed of sound [SOS]) were performed. Our initial results demonstrated that the simulated SWS values increased with the progression of liver fibrosis (from 1.5 m/s [Fibrosis stage 1] to 4 m/s [Fibrosis stage 4]). Similarly, the simulated SOS values were within the range of clinical data (from 1575 m/s [Fibrosis stage 0-3] to 1594 m/s [Fibrosis stage 4]). In summary, we found that those systems biology simulated fibrotic liver tissues with and without steatosis can reflect spatial characteristics of relevant histology. Also, their mechanical characteristics (i.e. shear/compressional wave speed) were in good agreement with data reported in the clinical literature.

Indexed as

Elasticity Imaging TechniquesFatty LiverLiverLiver CirrhosisModels, BiologicalSystems BiologyDisease ProgressionFemaleHumansMaleAgent-based modelingLiver fibrosisSteatosisUltrasound elastographyUltrasound tissue characterization

Identifiers

PMID30529712
PMCPMC6668994

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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.