Open AccessKnowledge domain and emerging trends in brachytherapy: A scientometric analysis
Author(s) -
Ghazbani Arash,
Abdolahi Mohammad,
Mansourzadeh Mohammad Javad,
BasirianJahromi Reza,
Behzadipour Sina,
Mohseni Azad Anali,
Talebzadeh Bardia,
Khosravi Abdolrasoul,
Hamidi Ali
Publication year - 2022
Publication title -
precision radiation oncology
Language(s) - English
Resource type - Journals
ISSN - 2398-7324
DOI - 10.1002/pro6.1171
Subject(s) - brachytherapy , domain (mathematical analysis) , citation , medical physics , data science , computer science , knowledge management , medicine , library science , radiation therapy , radiology , mathematics , mathematical analysis
Objective Assessing the current scientific situation helps to recognize the gaps and strengths of brachytherapy research projects. This research project was conducted to assess the knowledge domain and emerging trends in brachytherapy through a scientometric perspective. Methods For the present research, the Web of Science database was considered as the data source. Integrated data was transferred to Bibliometrix R Package V3.1. In this study, the scientometric approach was performed by CiteSpace 5.8.R3 to draw the trends and signify issues in the research area. Eventually, scientometric indicators were evaluated at the level of authors, documents, journals, organizations, and countries. Results A total of 31,362 documents from 64,740 Independent researchers were retrieved. The United States, Germany, and Canada were the most active countries in brachytherapy‐related research projects. In the present study, Luc Beaulieu, Christine Kirisits, and Ronald Nath were identified as the most influential authors. Eventually, keywords clusters were constructed by using the method of co‐citation analysis. In this case, the main clusters were cervical cancer and prostate cancer. Conclusion Assessing the scientific trends in brachytherapy indicated that new insights have been gained into this cancer treatment technique. In this case, development of computer applications and artificial intelligence alongside deep learning utilization provides new horizons for oncology and radiotherapy researchers.