ArticleScientific reports2018
Calibration of Multi-Parameter Models of Avascular Tumor Growth Using Time Resolved Microscopy Data.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin.Cancer biology & therapy · 2024Article
- Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation.Journal of mathematical biology · 2024Article
- Cancer cell sedimentation in 3D cultures reveals active migration regulated by self-generated gradients and adhesion sites.PLoS computational biology · 2024Article
- Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting.Journal of computational science · 2023Article
- Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis.PLoS computational biology · 2023Article
- Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.Biophysics reviews · 2022Review
- Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging.IEEE transactions on bio-medical engineering · 2021Article
- A Coupled Mass Transport and Deformation Theory of Multi-constituent Tumor Growth.Journal of the mechanics and physics of solids · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Two of the central challenges of using mathematical models for predicting the spatiotemporal development of tumors is the lack of appropriate data to calibrate the parameters of the model, and quantitative characterization of the uncertainties in both the experimental data and the modeling process itself. We present a sequence of experiments, with increasing complexity, designed to systematically calibrate the rates of apoptosis, proliferation, and necrosis, as well as mobility, within a phase-field tumor growth model. The in vitro experiments characterize the proliferation and death of human liver carcinoma cells under different initial cell concentrations, nutrient availabilities, and treatment conditions. A Bayesian framework is employed to quantify the uncertainties in model parameters. The average difference between the calibration and the data, across all time points is between 11.54% and 14.04% for the apoptosis experiments, 7.33% and 23.30% for the proliferation experiments, and 8.12% and 31.55% for the necrosis experiments. The results indicate the proposed experiment-computational approach is generalizable and appropriate for step-by-step calibration of multi-parameter models, yielding accurate estimations of model parameters related to rates of proliferation, apoptosis, and necrosis.
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.