ReviewIntegrative biology : quantitative biosciences from nano to macro2010
Modeling tumor growth and treatment response based on quantitative imaging data.
Review in Integrative biology : quantitative biosciences from nano to macro, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
- Modeling Tumor Growth Using Partly Conditional Survival Models: A Case Study in Colorectal Cancer.JCO clinical cancer informatics · 2023Trial
- Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy.Magnetic resonance in medicine · 2011Trial
- Phytohormones involved in vascular cambium activity in woods: current progress and future challenges.Frontiers in plant science · 2024Review
- Comparing mechanism-based and machine learning models for predicting the effects of glucose accessibility on tumor cell proliferation.Scientific reports · 2023Article
- Proving of a Mathematical Model of Cell Calculation Based on Apparent Diffusion Coefficient.Translational oncology · 2017Article
- An imaging-based computational model for simulating angiogenesis and tumour oxygenation dynamics.Physics in medicine and biology · 2016Article
- Towards the Personalized Treatment of Glioblastoma: Integrating Patient-Specific Clinical Data in a Continuous Mechanical Model.PloS one · 2015Article
- Serial diffusion MRI to monitor and model treatment response of the targeted nanotherapy CRLX101.Clinical cancer research : an official journal of the American Association for Cancer Research · 2013Article
- Incorporation of diffusion-weighted magnetic resonance imaging data into a simple mathematical model of tumor growth.Physics in medicine and biology · 2012Article
- Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer.ISRN biomathematics · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
We review current approaches to predicting tumor growth and treatment response that combine non-invasive imaging data with mathematical models of cancer progression, and propose some new directions for integrating quantitative imaging measurements with such numerical analyses. Historically, tumor modeling has been described by parameters that are measurable by invasive methods only or in isolated in vitro or ex vivo systems. This limits the practical usefulness of such models because it is not possible to test their predictions experimentally. Recent advances in three-dimensional magnetic resonance imaging, single photon emission computed tomography, and positron emission tomography techniques provide new opportunities to acquire measurements of relevant molecular and cellular features of tumors non-invasively and with high spatial resolution. Such data can be incorporated into mathematical models of tumors. We highlight some recent examples of this approach and identify several simple examples that allow for conventional mathematical models of tumor growth to be recast in terms of parameters that can be measured by imaging, thus raising the possibility of designing and constraining models that can be tested in clinical practice. It is our hope that this Perspective will stimulate further work in this evolving and exciting field.
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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.