ArticleIEEE transactions on medical imaging2010
Multiscale model of liver DCE-MRI towards a better understanding of tumor complexity.
Article in IEEE transactions on medical imaging, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review.Frontiers in physiology · 2021Pooled it
- Lumped parameter liver simulation to predict acute haemodynamic alterations following partial resections.Journal of the Royal Society, Interface · 2023Article
- A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures.Frontiers in bioinformatics · 2023Article
- Virtual clinical trials in medical imaging: a review.Journal of medical imaging (Bellingham, Wash.) · 2020Review
- Modeling of the contrast-enhanced perfusion test in liver based on the multi-compartment flow in porous media.Journal of mathematical biology · 2018Article
- GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making.Journal of digital imaging · 2013Article
- BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems.Journal of pharmacokinetics and pharmacodynamics · 2012Article
- Vascular system modeling in parallel environment - distributed and shared memory approaches.IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society · 2011Article
- Modeling tumor growth and treatment response based on quantitative imaging data.Integrative biology : quantitative biosciences from nano to macro · 2010Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of quantitative imaging for the characterization of hepatic tumors in magnetic resonance imaging (MRI) can improve the diagnosis and therefore the treatment of these life-threatening tumors. However, image parameters remain difficult to interpret because they result from a mixture of complex processes related to pathophysiology and to acquisition. These processes occur at variable spatial and temporal scales. We propose a multiscale model of liver dynamic contrast-enhanced (DCE) MRI in order to better understand the tumor complexity in images. Our design couples a model of the organ (tissue and vasculature) with a model of the image acquisition. At the macroscopic scale, vascular trees take a prominent place. Regarding the formation of MRI images, we propose a distributed model of parenchymal biodistribution of extracellular contrast agents. Model parameters can be adapted to simulate the tumor development. The sensitivity of the multiscale model of liver DCE-MRI was studied through observations of the influence of two physiological parameters involved in carcinogenesis (arterial flow and capillary permeability) on its outputs (MRI images at arterial and portal phases). Finally, images were simulated for a set of parameters corresponding to the five stages of hepatocarcinogenesis (from regenerative nodules to poorly differentiated HepatoCellular Carcinoma).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.