ReviewNature reviews. Cancer2015
The mathematics of cancer: integrating quantitative models.
Review in Nature reviews. Cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 280 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
280 citing papers in PubMed.
- Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data.Cancer research · 2019Trial
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- Review
- Comparison of volumetric dynamic optical coherence tomography with biological methods for evaluation of radiation effects in prostate tumor spheroids.Journal of biomedical optics · 2026Article
- Quantitative dissection of the metastatic cascade at single colony resolution.Research square · 2026Article
- Article
- Computational Oncology of Chemotaxis-Driven Tumour-Immune Spatial Patterning and Stability.Bioengineering (Basel, Switzerland) · 2026Article
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Article
- Review
- Improvement in model flexibility reveals signaling pathway in T cell responses to pulsatile stimuli.NPJ systems biology and applications · 2026Article
- Integrating Kolmogorov-Arnold networks with ordinary differential equations for efficient, interpretable, and robust deep learning: Epidemiology of infectious diseases as a case study.Infectious Disease Modelling · 2026Article
- A new cancer progression model: From synthetic tumors to real data and back.PLoS computational biology · 2026Article
- Multifactorial sheltering in peristromal niches shapes in vivo responses of lung cancers to targeted therapies.Nature communications · 2026Article
- Multiscale predictive cellular modeling: integrating hypothesis grammars, digital twins, and multi-omics for In silico oncology and precision theranostics.Functional & integrative genomics · 2026Review
- Exploring the relationship between vascular remodelling and tumour growth using agent-based modelling.PLoS computational biology · 2026Article
- Mathematical modeling of ribosome competition informs testable treatment strategies for drug-tolerant cancer persister cells.iScience · 2026Article
- Article
- Stochastic Evolutionary Control in Heterogeneous Populations.bioRxiv : the preprint server for biology · 2026Article
- Fractional spatiotemporal Hahnfeldt tumor model with convergence analysis and optimal control.Scientific reports · 2026Article
220 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.
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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.