Bibliometrics Analysis of Diagnostic Test Accuracy Studies of Bladder Cancer.

backgroundBladder cancer is amongst the top 10 most common neoplasms worldwide. It is responsible for over 150,000 deaths per annum. It is of great importance to study its clinical diagnosis and management. As a literature synthesis technique, bibliometrics analysis helps us to take our knowledge of bladder cancer diagnosis research, topics, and trends a step further. It is critical to elucidate the literature landscape and its pertinent impact on this field, in order to have a better understanding and improved management of bladder cancer. MATERIALS AND

methodsSearch terms related to bladder cancer diagnosis were used to retrieval publications which met the predefined criteria in the Scopus database developed by Elsevier. Included articles were further evaluated by year of publication, country, language, institution, article type, source journal, coauthorship networks, and text mining of titles. The R software, as well as the

resultsA comprehensive literature search in the Scopus database returned a total of 824 publication items. Among them, 601 (72.94%) were published as journal articles, and 117 (14.20%) were published as reviews. The number of annual publications in this field has been increasing in recent years. English represents the language used in the majority of the articles (634, 77.13%). The highest number of citations received for a single article is as high as 628 times. We also clustered and visualized a network of 701 authors with no less than 3 publications. The term cloud and hierarchical clustering dendrogram which revealed with text mining were used to discover the hot research topics in this field. We also statistically analyzed evolution of the major journal impact indices, including Impact Factor, EigenFactor Score, and CiteScore of 11 journals with a period of 9 years.

conclusionThrough bibliometrics analysis, we found hot topics and related research focuses and trends in bladder cancer diagnosis. This bibliometrics analysis has identified influential articles in the field and provides a useful guide to researchers as to what type of article constitutes a highly citable publication in this subject. In addition, a coauthorship network helps researchers find out which team may be a potential partner and where their studies focus on.