ArticleBMC bioinformatics2019
Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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
5 citing papers in PubMed.
- Data-driven spatio-temporal modelling of glioblastoma.Royal Society open science · 2023Review
- Hybrid computational models of multicellular tumour growth considering glucose metabolism.Computational and structural biotechnology journal · 2023Review
- Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI.Medical & biological engineering & computing · 2022Article
- Simulation of 3D centimeter-scale continuum tumor growth at sub-millimeter resolution via distributed computing.Computers in biology and medicine · 2021Article
- Publisher Correction to: Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study.BMC bioinformatics · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
backgroundContemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments.
resultsThis paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered.
conclusionIn silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations.
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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.