Introduction: With the explosive increase in medical publications, the need of automatic or semi-automatic filtering mechanisms for literature collection in the systematic review (SR) process rise. We propose a SR example based on the methodology of knowledge domain visualization (KDV).

Material and Method: BIA-ALCL is one of the main knowledge clusters based on our previous report of all 14 years articles of Aesthetic Surgical Journal. Based on the finding of the knowledge “skeleton” of two MeSH terms “Breast implants” and “Lymphoma, Large-Cell, Anaplastic”, the literature for our SR was retrieved from PubMed.

Results: 496 articles were retrieved from 1996 to 2022. The publications rose rapidly after the 2008 JAMA article of de Jong, D., et al. 1,064 MeSH terms of the retrieved articles were analyzed. According to our surgical point of view, the MeSH topic could be grouped to three groups, and their frequency were shown – 1) THE COMMON TERMS, eg. “Breast Implantation” (260 as major topics), “Female” (384), “Postoperative complications” (107, 55 in which is labeled as major topics), “Seroma” (82, 43 as major); 2) THE INTERMEDIATE TERMS, eg. “Contracture” and “Implant Capsular Contracture” (39, 21 as major) , “Device removal” (37), “Prosthesis design” (31, 29 as major), “Surface properties” (12),; and 3) THE RARE FINDINGS, eg. “Biofilms” (17, 11 as major), “Informed consent” (10, 7 as major), “Disease” and “Disease progression” (8).

Conclusion: We offered a feasible SR process based on the findings from a unbiased KDV technology and then the analysis of the MeSH details of our topic, which made the systematic literature management more systematic.

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