ReviewCPT: pharmacometrics & systems pharmacology2019
A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors.
Review in CPT: pharmacometrics & systems pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 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.
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Who cites it
77 citing papers in PubMed, 187 citations in OpenAlex.
- Joint modeling of tumor dynamics and progression-free survival in advanced breast cancer: Leveraging data from amcenestrant early phase I-II trials.CPT: pharmacometrics & systems pharmacology · 2024Trial
- Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment.CPT: pharmacometrics & systems pharmacology · 2022Trial
- Early predictions of response and survival from a tumor dynamics model in patients with recurrent, metastatic head and neck squamous cell carcinoma treated with immunotherapy.CPT: pharmacometrics & systems pharmacology · 2021Trial
- The Sleeping Giant: Metabolism and Autophagy in the Generation and Survival of Dormant Polyploid Giant Cancer Cells.MedComm · 2026Review
- Bridging BET bromodomain and immune checkpoint inhibitors through generative bioorganic frameworks for next-generation cancer immunotherapy.RSC medicinal chemistry · 2026Review
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Internet of Things-Enhanced Mathematical Oncology: Conceptual Framework for Adaptive Cancer Care Modeling.JMIR cancer · 2026Article
- Evaluating neoantigen-vaccine responses through mechanistic and model-based frameworks.NPJ precision oncology · 2026Article
- A new cancer progression model: From synthetic tumors to real data and back.PLoS computational biology · 2026Article
- Follow-Up Bias in Tumor Dynamic Modeling: A Comparison of Classical and Neural-ODE Approaches.CPT: pharmacometrics & systems pharmacology · 2026Article
- Time-Varying Hormonal Treatment and Metastasis-Free Survival Among ER+ Breast Cancer Patients: A Natural History Modelling Approach.Statistics in medicine · 2026Article
- ViVo: A Temporal Modeling Framework That Boosts Statistical Power and Minimizes Animal Usage.Journal of medicinal chemistry · 2026Article
- Towards the tumor microenvironment scoring methods for immune checkpoint inhibitor response.NPJ precision oncology · 2026Article
- A hybrid Ornstein-Uhlenbeck-Branching framework unifies microbial and pediatric tumor evolution.Frontiers in oncology · 2026Article
- The theory of tumor immuno-ecodynamics.Frontiers in immunology · 2026Article
- A Systematic Comparative Analysis of Tumor Size Models Based on Erlotinib Clinical Data in Advanced NSCLC.CPT: pharmacometrics & systems pharmacology · 2025Article
- Evaluation and Mitigation of Time-Dependent Confounding Effects in Conventional Exposure-Response Analyses for Oncology Drugs.CPT: pharmacometrics & systems pharmacology · 2025Article
- Deep learning-enabled fluorescence imaging for oral cancer margin classification in preclinical models.Journal of biomedical optics · 2025Article
- Review
- Human preclinical multiple myeloma in vitro models for disease modeling and therapy screening.Journal of biological engineering · 2025Review
17 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model-based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model-based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model-based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.
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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.