Abstract
PurposeThough literature related to Cushing's disease (CD) has grown significantly, previous reviews exclusively focused on specific research areas and were biased towards highly cited articles. This study aims to systemically analyze the research landscapes and trends using unbiased methods. MethodsWe queried all the CD-related publications in PubMed and clinical trials registered on clinicaltrials.gov. Latent Dirichlet allocation (LDA), a machine learning method, was used to derive research hotspots from article texts. The research topic clusters and country-level collaboration were revealed by network analysis.Results5015 articles were published since 1981, currently growing at 155 per year, with more retrospective studies but fewer prospective studies. Interestingly, the most popular LDA research topics were complications and comorbidities, endocrine hormone tests and surgical therapy, and they formed a remarkable triangle relationship in the research topic network. These topics had numerous international studies and were supported by most funding. In addition, many topics in the basic research domain were proliferating, including mutation, biomarkers, endopeptidases, and other molecular genetics and pathology of CD. Out of 63 registered clinical trials, over 25% were withdrawn due to inadequate patient recruitment or lack of funding.ConclusionsThis publication landscape analysis provided a systemic representation of CD literature regarding the history, current challenges, and future directions, enabling clinicians a rapid and comprehensive insight into the disease.