Introduction: The purpose of this study was to assess the landscape of parathyroid carcinoma research during the last 22 years using machine learning and text analysis.
Method: In November 2021, we obtained from PubMed all works indexed under the mesh subject line "parathyroid carcinoma". The entire set of search results was retrieved in XML format, and metadata such as title, abstract, keywords, mesh words, and year of publication were extracted for bibliometric evaluation from the original XML files. To increase the specificity of the investigation, the Latent Dirichlet allocation (LDA) topic modeling method was applied.
Results: The paper analyzed 3578 papers. The volume of literature related to parathyroid cancer has been relatively flat over the past 22 years. In the field of parathyroid cancer research, the most important topic of clinical interest is the differential diagnosis. Ultrasound and MIBI are the most commonly used imaging methods for localization. In terms of basic research, the mechanisms of gene mutation and local tumor recurrence are the focus of interest.
Conclusion: There are huge unmet research needs for parathyroid carcinoma. Improving the diagnosis rates of parathyroid cancer by clinicians and establishing new and reliable molecular pathological markers and new image localization techniques will continue to be the focus of future research.
Keywords: PubMed; machine learning; natural language processing; parathyroid carcinoma; publication analysis.
Copyright © 2022 Feng, Tian, Huang, Chen, Feng and Chang.