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

TL;DR

This bibliometric study analyzes the evolution, interdisciplinarity, and collaboration patterns in mathematical oncology, highlighting its growth since the 1960s and emphasizing the importance of international cooperation and diverse research topics.

Contribution

It provides a comprehensive bibliometric analysis of mathematical oncology, comparing it to mathematical biology, and offers insights into its interdisciplinary and international development.

Findings

Mathematical oncology has expanded significantly since the 1960s.

Increased international collaboration and larger research teams are evident.

Diverse research topics reflect adaptation to big data and machine learning.

Abstract

Mathematical oncology is an interdisciplinary research field where the mathematical sciences meet cancer research. Being situated at the intersection of these two fields makes mathematical oncology highly 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.