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

TL;DR

This study addresses the challenge of delimiting mathematical oncology and its evolving boundaries by applying a two-stage, journal-first bibliometric analysis to 1961–2024 data from five core mathematical biology journals. It quantifies interdisciplinarity, internationality, collaboration, and topic trends, and benchmarks against mathematical biology. Key contributions include the identification of globalisation patterns, the importance of international collaboration networks, and the differential thematic evolution of data-driven clinical orientation versus theoretical modelling. The findings have practical implications for funding, education, and science communication, and are complemented by open code and data resources to support ongoing monitoring of the field.

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.

Figures (6)

• Figure 1: Article selection and classification. (a) The topic search (TS) query used in the WoSCC to identify mathematical oncology articles and inform the journal selection. (b) The data collection, cleaning and classification procedure. (c) The proportion of candidate keyword groups (WG) 1-3 proposed in the article classification, with article counts given after the colons. (d) Annual article counts for the focus journals following the article classification in (b).
• Figure 2: Interdisciplinarity in mathematical oncology. (a) The citation matrix shows how often mathematical oncology* articles (rows) are cited by mathematical biology* articles (columns). The right-most column shows the number of row-wise citations from all journals (including those beyond our focus journals). The total number of mathematical oncology* articles per journal are shown in row-legend parentheses, and the total number of mathematical biology* articles per journal are shown in column-legend parentheses. The maximum number of citations per row are red and underlined. (b) The citation-V shows the percentage of articles in different disciplinary categories that are cited by mathematical oncology* (left), and that cite mathematical oncology* (right) for all focus journals between 1961 and 2024. (c) The results in (b) are shown for one focus journal at a time in pie charts. (d) The results in (b) are shown over time. For each citation, it is the time (year) that the citation was made that is shown in one of the plots. (e) Annual counts for articles that are cited by (top) and that cite (bottom) mathematical oncology*. Counts for both discipline-classified and unclassified journals are shown. The notation mathematical oncology* refers to the studied dataset.
• Figure 3: Internationality in mathematical oncology. The box plots show the number of unique author affiliation-countries per article over time for (a) all articles in the datasets, and (b) articles with above-average citations published that year. In the box plots, black lines indicate the medians; boxes show the interquartile ranges (IQR); whiskers extend to the smallest and largest data points within 1.5×IQR from the box edges; and black dots represent outliers, with dot size proportional to their frequency. (c) The heatmaps show country-wise contributions (number of articles) to the mathematical oncology* dataset for three different time eras. Red lines demonstrate pairwise collaborations between countries, where the line thickness is proportional to collaboration frequency. Heatmap colours and line thicknesses are normalised for each time period. The notations mathematical oncology* and biology* refer to the studied datasets.
• Figure 4: Collaboration in mathematical oncology. The box plots show the number of authors per article over time for (a) all articles in the datasets and (b) articles with above-average citations published that year. In the box plots, black lines indicate the medians; boxes show the interquartile ranges (IQR); whiskers extend to the smallest and largest data points within 1.5×IQR from the box edges; and black dots represent outliers, with sizes proportional to their frequency. The notations mathematical oncology* and biology*, refer to the studied datasets.
• Figure 5: Trending topics in mathematical oncology analysed through title word frequency. In each subplot, the bar chart shows the frequency of the collective word cloud terms. The word clouds contain the 25 most frequent terms with sizes proportional to their frequency. Data for all mathematical oncology* abstracts, per time period, are shown in the top row. Data for articles with at least one citation in mathematics, focus, and life science journals are shown in the three rows below, respectively.