Magnetic Resonance Imaging and Artificial Intelligence in Rectal Cancer Staging: A Bibliometric Analysis Using VOSviewer

Objective: This study aims to conduct a bibliometric analysis of publications on MRI-based staging of rectal cancer and the applications of artificial intelligence (AI). The study examines key trends in the literature, leading authors, and collaboration networks. Material and Methods: Data were retrieved from the Web of Science (WoS) Core Collection for the period between 2014 and 2024. Citation analysis, co-authorship mapping, bibliographic coupling, and keyword co-occurrence analysis were performed using VOSviewer software. A minimum threshold of two publications and five citations was applied in the analyses. Network, overlay, and density maps were generated. Results: A total of 104 publications were analyzed. The findings indicate that Cui, Cusumano, and Li are among the most frequently cited authors in the field of MRI and AI applications for rectal cancer. Prominent keywords include “rectal cancer,” “magnetic resonance imaging,” “deep learning,” and “machine learning,” reflecting high research activity in these areas. In contrast, terms such as “transfer learning” and “tumor budding” appeared less frequently. Strong collaboration networks were identified, particularly between China and the Netherlands, with GE Healthcare emerging as a key institution in the field. However, the integration of traditional MRI protocols with AI-driven analyses remains limited, highlighting a critical research gap that warrants further exploration. Conclusion: This bibliometric analysis identifies influential themes and leading researchers in MRI and AI applications for rectal cancer. Future research should focus on integrating traditional MRI protocols with AI algorithms and strengthening international collaboration to advance the field.