ArticlebioRxiv : the preprint server for biology2026
75 Years of Mathematical Oncology.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
The trial behind it
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Authors and funding
12 authors.
Funding
Abstract
Mathematics has long provided a quantitative framework to interpret cancer biology and treatment. Driven by richer biological and clinical data, the field has evolved into a multidisciplinary and translational endeavor - giving rise to Mathematical Oncology. Yet, the field's strong interdisciplinarity obscures a comprehensive view of its evolution, as well as the boundaries between Mathematical Oncology and adjacent domains. Here, we address this gap through a comprehensive bibliometric analysis spanning 75 years of mathematical research in oncology. Using a manually curated corpus (~1,500 papers) and a large query-based dataset (~19,000 papers), we map the field's conceptual and collaborative development over time. Independent analyses reveal sustained growth, high impact, and pronounced interdisciplinarity, together with a gradual shift from fundamental cancer biology toward therapeutic modeling. This reorientation underpins the emergence of Mathematical Oncology as a distinct field, separate from Systems Biology and Pharmacokinetics / Pharmacodynamics. We show how diverse concepts interlace within Mathematical Oncology, bridging applied mathematics, optimal control, evolutionary theory, and imaging. Collectively, we demonstrate that Mathematical Oncology is not merely the application of mathematics to cancer, but the use of interpretable models integrating clinical, biological, and physical knowledge to improve screening, understand disease evolution, guide therapy, and strengthen forecasting.
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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.