ReviewPhysical biology2019
The 2019 mathematical oncology roadmap.
Review in Physical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 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
99 citing papers in PubMed.
- Towards a clinically practical computational platform for systematically adapting radiation therapy for glioma patients.Physics in medicine and biology · 2026Article
- Computational Oncology of Chemotaxis-Driven Tumour-Immune Spatial Patterning and Stability.Bioengineering (Basel, Switzerland) · 2026Article
- In silico clinical trials of BiTE expression by oncolytic viruses reveal the impact of patient heterogeneity on dosage protocol.PLoS computational biology · 2026Article
- Growth rate-driven modelling suggests that phenotypic adaptation drives drug resistance in BRAFV600E-mutant melanoma.Communications biology · 2026Article
- 75 Years of Mathematical Oncology.bioRxiv : the preprint server for biology · 2026Article
- Optimizing breast cancer treatment strategies through fractional-order dynamics: A computational modeling approach.PloS one · 2026Article
- Article
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- A bibliometric study on mathematical oncology: interdisciplinarity, internationality, collaboration and trending topics.Bulletin of mathematical biology · 2025Article
- Article
- A deep learning model to predict glioma recurrence using integrated genomic and clinical data.Communications medicine · 2025Article
- Beyond RECIST: mathematical modeling and Bayesian inference reveal the importance of immune parameters in metastatic breast cancer.bioRxiv : the preprint server for biology · 2025Article
- A data assimilation framework for predicting the spatiotemporal response of high-grade gliomas to chemoradiation.BMC cancer · 2025Article
- Identifiability of phenotypic adaptation from low-cell-count experiments and a stochastic model.PLoS computational biology · 2025Article
- Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Tumor Growth, Proliferation and Diffusion in Osteosarcoma.Acta biotheoretica · 2025Article
- Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer.NPJ systems biology and applications · 2025Article
- Mathematical modeling of the effect of early detection in breast cancer treatment.Frontiers in oncology · 2025Article
- Simulation approach for common female cancers: a brief review.Frontiers in oncology · 2025Review
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
39 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
35 authors.
Funding
Abstract
Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.
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.