Mapping the global research landscape and emerging trends in tumor treating fields for cancer therapy

This study aims to map the structural landscape, collaborative networks, and thematic evolution of Tumor Treating Fields (TTFields) research via quantitative bibliometric analysis. By interpreting dataset structures rather than providing a traditional narrative review, it seeks to elucidate global research trends, identify key contributors, and uncover the thematic evolution shifting from basic mechanistic validation to clinical combination strategies. A comprehensive bibliometric analysis was performed on 704 publications related to TTFields published between 2004 and 2025. Data were exclusively retrieved from the Web of Science Core Collection (WoSCC) database. Quantitative analysis and visualization mapping were systematically executed using the bibliometrix R package, VOSviewer, and CiteSpace to assess publication trends, collaborative networks, and keyword citation bursts. TTFields, a non-invasive anti-cancer treatment employing intermediate-frequency alternating electric fields, has demonstrated definitive efficacy in glioblastoma (GBM) and malignant pleural mesothelioma, while retaining potent investigational prospects in other solid tumors (including non-small cell lung cancer, pancreatic cancer, and ovarian cancer). Network analyses reveal that the foundational high-impact literature is predominately driven by a highly concentrated, industry-academic collaborative structure. Furthermore, keyword burst detection identifies a critical field transition from initial phase-III trial outcomes toward prioritizing clinical “safety” and exploring novel “immunotherapy” combinations. This study provides a visualized quantitative landscape of TTFields research. It highlights TTFields as an innovative and highly translational treatment modality. While current advancements have been efficiently accelerated by industry-sponsored networks, moving forward will increasingly require independent mechanistic verifications. The findings emphasize the necessity of addressing long-term post-market safety, overcoming drug delivery barriers, and fostering interdisciplinary collaborations—such as mathematical modeling and AI-assisted network pharmacology—to optimize future combinatorial therapies.