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This paper is in the following e-collection/theme issue:

Clinical Cancer Research (240) New Methods (1786) Innovations and Technology in Cancer Research (78) Machine Learning (3151) Digital Health Meta-Research and Bibliographic Studies (117) Nephrology/Renal Disorders (138)

Published on in Vol 12 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78797, first published .
Young man receiving IV treatment in a medical chair.

Using Latent Dirichlet Allocation Topic Modeling to Uncover Latent Research Topics and Trends in Renal Cell Carcinoma: Bibliometric Review

Using Latent Dirichlet Allocation Topic Modeling to Uncover Latent Research Topics and Trends in Renal Cell Carcinoma: Bibliometric Review

Authors of this article:

Javier De La Hoz-M1 Author Orcid Image ;   Karime Montes-Escobar2, 3 Author Orcid Image ;   Carlos Alfredo Salas-Macias4 Author Orcid Image ;   Martha Fors5 Author Orcid Image ;   Santiago J Ballaz6 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Montes-Escobar K, de La Hoz-Maestre J, Llinás-Solano H, Salas-Macias C, Fernández-Moreira E, Fors M, Ballaz S. Basal Cell Carcinoma Research Landscape Overview via Latent Dirichlet Allocation and HJ-Biplot Analysis. Cancers 2026;18(14):2312 View
  2. Li K, Xiang Y, Zhang L, Shi L, Qian W, Zhu M. Bibliometric and LDA analysis of the correlation between childhood obesity and precocious puberty (2005–2025). Frontiers in Pediatrics 2026;14 View