Visualization of research trend of neoadjuvant chemotherapy for breast cancer treatment: a bibliometric analysis.

PURPOSE

Neoadjuvant chemotherapy (NAC) is a cornerstone in the treatment of breast cancer, aimed at shrinking tumors preoperatively and improving surgical outcomes. Although the literature volume has shown an annual growth trend, no comprehensive bibliometric and clinical analysis covering multiple databases in this field is available. The study aims to identify key contributions to the field and provide insights for future research directions.

METHODS

The articles were obtained from the Web of Science Core Collection (WoSCC) on breast cancer in the past decades (from 1999 to 2024). VOSviewer 1.6.17, CiteSpace 5.8.R.1 and package "bibliometrix" were used to conduct this bibliometric analysis.

RESULTS

We included 11,505 articles and the top 100 cited journals from WOSCC for analysis. The USA had the highest number of publications, and most of the top 100 cited articles were from the USA. Sibylle Loibl, Michael Untch, and Von Minckwitz G have made significant contributions through their high research productivity and the publication of high-quality articles; Von M was the most cited author from the top 100 cited journals. Keyword co-occurrence studies suggested that the research hotspots in the field of NAC for breast cancer focus on survival rate, preoperative chemotherapy, and overall treatment strategy. Increased clinical trials and randomized trials on preoperative chemotherapy and the use of positron emission tomography, especially for triple-negative breast cancer (TNBC), have attracted wide attention and research in the academic community over a specific period.

CONCLUSION

This study presents a bibliometric analysis on NAC for breast cancer, highlighting research trends, influential studies, and collaborative networks. This data analysis highlights key research gaps in NAC for breast cancer, including limited international collaboration, underexplored treatment strategies for aggressive subtypes like TNBC, and the need for more high-quality trials to optimize personalization and response prediction.