ReviewTrends in cancer2022
Optimizing the future: how mathematical models inform treatment schedules for cancer.
Review in Trends in cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review on the culture methods and applications of 3D tumoroids for cancer research and personalized medicine.Cellular oncology (Dordrecht, Netherlands) · 2025Pooled it
- Potential Role of mRNA in Estimating Postmortem Interval: A Systematic Review.International journal of molecular sciences · 2024Pooled it
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Single-threshold-guided adaptive cancer therapy with partial-cycle treatment: A mechanistic and reinforcement learning analysis.PLoS computational biology · 2026Article
- Adaptive and sequential cancer therapies emerge from treatment schedule optimization.Research square · 2026Article
- Rebinding drives cellular retention of small-molecule drugs with abundant targets.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Cancer Evolvability Determines Therapy Outcomes.bioRxiv : the preprint server for biology · 2026Article
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- 75 Years of Mathematical Oncology.bioRxiv : the preprint server for biology · 2026Article
- Computational modeling and simulation in oncology.Clinical and translational medicine · 2025Article
- Ordinary differential equation model of cancer-associated fibroblast heterogeneity predicts treatment outcomes.NPJ systems biology and applications · 2025Article
- A statistical framework for detecting therapy-induced resistance from drug screens.NPJ systems biology and applications · 2025Article
- Optimizing the Future: A Game Theory to Tumor Therapeutic Strategies.Biological procedures online · 2025Article
- Mathematical modeling for glioblastoma treatment: scenario generation and validation for clinical patient counseling.Frontiers in oncology · 2025Article
- Article
- N-of-1 medicine.Singapore medical journal · 2024Article
- Predictive, preventive, and personalized medicine in breast cancer: targeting the PI3K pathway.Journal of translational medicine · 2024Review
- Treatment of evolving cancers will require dynamic decision support.Annals of oncology : official journal of the European Society for Medical Oncology · 2023Review
- Fractional calculus in mathematical oncology.Scientific reports · 2023Article
- Understanding the Mechanisms and Treatment of Heart Failure: Quantitative Systems Pharmacology Models with a Focus on SGLT2 Inhibitors and Sex-Specific Differences.Pharmaceutics · 2023Review
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
For decades, mathematical models have influenced how we schedule chemotherapeutics. More recently, mathematical models have leveraged lessons from ecology, evolution, and game theory to advance predictions of optimal treatment schedules, often in a personalized medicine manner. We discuss both established and emerging therapeutic strategies that deviate from canonical standard-of-care regimens, and how mathematical models have contributed to the design of such schedules. We first examine scheduling options for single therapies and review the advantages and disadvantages of various treatment plans. We then consider the challenge of scheduling multiple therapies, and review the mathematical and clinical support for various conflicting treatment schedules. Finally, we propose how a consilience of mathematical and clinical knowledge can best determine the optimal treatment schedules for patients.
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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.