Logistic tumor-population growth and ghost-points symmetry
Pasetto, S.; Harshe, I.; Brady, R.; Gatenby, R.; Enderling, H.
Show abstract
The observed time evolution of a population is well approximated by a logistic function in many research fields, including oncology, ecology, chemistry, demography, economy, linguistics, and artificial neural networks. Initial growth is exponential at a constant rate and capped at a limit size, i.e., the carrying capacity. In mathematical oncology, the carrying capacity has been postulated to be co-evolving and thus patient-specific. As the relative tumor-over-carrying capacity ratio may be predictive and prognostic for tumor growth and treatment response dynamics, it is paramount to estimate it from limited clinical data. We show that exploiting the logistic functions rotation symmetry can help estimate the populations growth rate and carry capacity from fewer data points than conventional regression approaches. We test this novel approach against a classic oncology database of logistic tumor growth, achieving a 30% to 40% reduction in the time necessary to correctly estimate the logistic growth rate and carrying capacity. Our results will improve tumor dynamics forecasting and augment the clinical decision-making process.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Statistical inference of mechanistic models from qualitative data using an efficient optimal scaling approach 97%
- Identifiability analysis for models of the translation kinetics after mRNA transfection 97%
- Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma 96%
Similar papers in this journal
- Efficient inference for agent-based models of real-world phenomena 98%
- EpiLPS: a fast and flexible Bayesian tool for near real-time estimation of the time-varying reproduction number 97%
- Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.