75 Years of Mathematical Oncology

Pradelli, F.; Strobl, M.; Marzban, S.; de Kermenguy, F.; Barnett, A.; Ganesan, K.; Lorenzo, G.; Hormuth, D. A.; Hamis, S.; Bhaskar, D.; Anderson, A. R. A.; West, J.

doi:10.64898/2026.01.13.699306

bioRxiv · 10.64898/2026.01.13.699306

75 Years of Mathematical Oncology

Pradelli, F.·Strobl, M.·Marzban, S.·de Kermenguy, F.·Barnett, A.·Ganesan, K.·Lorenzo, G.·Hormuth, D. A.·Hamis, S.·Bhaskar, D.·Anderson, A. R. A.·West, J.

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Abstract

Constructing a comprehensive overview of any scientific field requires accurate literature selection, yet conventional keyword-based searches are susceptible to false positives. This problem is magnified in growing or interdisciplinary fields such as mathematical modeling in oncology that contain a rich but heterogeneous body of literature. Here, a generalizable, context-enriched artificial intelligence pipeline based on large language models (LLMs) curates large scientific corpora according to a user-defined field: mathematical modeling in oncology (>35k publications). Benchmarking against expert evaluation demonstrates high accuracy (ROC AUC[~]0.95) and agreement with human judgement (correlation[~]0.68), outperforming zero-shot LLM curation Analysis of the curated corpus ([~]14k) suggests that Mathematical Oncology s distinct from either Systems Biology and Pharmacokinetics/Pharmacodynamics despite employing overlapping methods. Co-occurring citation network analysis defines nine research clusters focused on a range of applications including drug delivery, optimal control, stochastic modeling, tumor microenvironment, radiation, cancer evolution, and spatial multiscale modeling. Significance StatementA generalizable, context-enriched artificial intelligence pipeline accurately curates large scientific corpora Analysis of the curated dataset applied to the se of mathematics n oncology provides comprehensive view of mathematical modeling in oncology across 140 years, revealing its shift from fundamental cancer biology towards therapeutic modelling.

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Pradelli, F., Strobl, M., Marzban, S., de Kermenguy, F., Barnett, A., Ganesan, K., Lorenzo, G., Hormuth, D. A., Hamis, S., Bhaskar, D., Anderson, A. R. A., West, J.. 2026-01-14. 75 Years of Mathematical Oncology. https://doi.org/10.64898/2026.01.13.699306

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