Editorial: Quantitative insights into new cancer therapies: a mathematical modeling approach

Journal article : Editorial

The development of new anti-cancer therapies is time-intensive and costly, and traditionally relies on extensive laboratory and clinical experimentation. To complement these efforts, mathematical and computational approaches are increasingly used to address key research questions, reduce experimental burden, and guide decision-making. In oncology, a range of emerging treatment modalities-including immunotherapy, gene therapy, targeted therapies, nanomedicine, and oncolytic virotherapy-represe...

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Authors: Malinzi, J; Byrne, HM

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Frontiers Media
• Journal: Frontiers in Applied Mathematics and Statistics
• Volume: 12
• Article number: 1869506
• Publication date: 2026-05-28
• Acceptance date: 2026-05-13
• DOI: 10.3389/fams.2026.1869506
• EISSN: 2297-4687
• ISSN: 2297-4687
• Language: English
• Keywords: causal inference; immunotherapy; agent-based models; cancer modeling; equation learning; oncolytic virotherapy; mathematical oncology; sequential treatments
• Subtype: Editorial