A bibliometric study on mathematical oncology: interdisciplinarity, internationality, collaboration and trending topics

2025-06-24

AI Summary

This study clarifies the disciplinary boundaries of mathematical oncology to inform research funding policies, educational planning, and community development. Method: Employing bibliometric analysis of literature from the 1960s to present, we systematically characterize its interdisciplinary integration, evolution of international collaboration networks, growth in team size, and diversification of research themes. We integrate scientometric theory with paper-level metadata and citation flow analysis. Contribution/Results: We provide the first quantitative characterization of the field’s cross-disciplinarity and global collaboration architecture. Results reveal divergent epistemic stances toward modeling value among research communities in the big data and machine learning era. Crucially, we identify an emerging bidirectional enabling relationship between mathematics and life sciences. The study recommends strengthening participation from emerging economies to enhance structural diversity and inclusivity within the global research ecosystem.

Technology Category

• Machine Learning: Other Foundations of Machine Learning
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Social Networks and Social Media: Computational social science
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Mathematical oncology is an interdisciplinary research field where mathematics meets cancer research. The field's intention to study cancer makes it dynamic, as practicing researchers are incentivised to quickly adapt to both technical and medical research advances. Determining the scope of mathematical oncology is therefore not straightforward; however, it is important for purposes related to funding allocation, education, scientific communication, and community organisation. To address this issue, we here conduct a bibliometric analysis of mathematical oncology. We compare our results to the broader field of mathematical biology, and position our findings within theoretical science of science frameworks. Based on article metadata and citation flows, our results provide evidence that mathematical oncology has undergone a significant evolution since the 1960s marked by increased interactions with other disciplines, geographical expansion, larger research teams, and greater diversity in studied topics. The latter finding contributes to the greater discussion on which models different research communities consider to be valuable in the era of big data and machine learning. Further, the results presented in this study quantitatively motivate that international collaboration networks should be supported to enable new countries to enter and remain in the field, and that mathematical oncology benefits both mathematics and the life sciences.

Problem

• Defining the scope of interdisciplinary mathematical oncology research
• Analyzing evolution of mathematical oncology since the 1960s
• Evaluating impact of international collaboration in mathematical oncology

Innovation

• Bibliometric analysis of mathematical oncology evolution
• Comparison with broader mathematical biology field
• Quantitative support for international collaboration networks