The translational chasm in machine learning for triple-negative breast cancer: a quantitative landscape assessment.

Background: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and the one with the fewest therapeutic targets. The application of machine learning (ML) in TNBC research is becoming increasingly widespread; however, systematic bibliometric studies focusing on this rapidly evolving interdisciplinary field are still lacking. Methods: This study systematically searched the Web of Science Core Collection and Scopus databases for relevant literature on the application of ML in the field of TNBC published between January 2011 and January 2026. These publications were analyzed using VOSviewer, CiteSpace, and the R/Bibliometrix package. An independent external validation was conducted using the same search strategy in the PubMed database. Additionally, clinical trials and randomized controlled trials were identified through PubMed to quantify the clinical translation gap in this field. Results: We conducted a bibliometric analysis of 394 publications and found that global publication output exhibits a nonlinear growth trend. China ranked first in publication volume with 180 papers, while the United States ranked first in academic influence with an average of 39.09 citations per paper; the collaboration network revealed a global pattern with China and the United States as dual hubs. Co-occurrence analysis of keywords identified six major thematic clusters, broadly corresponding to three domains: disease understanding, methodological innovation, and clinical translation. Citation emergence analysis revealed that "deep learning," "radiomics," and "tumor-infiltrating lymphocytes" represent the most robust emerging frontiers at present. Cluster analysis of co-cited literature identified three dominant research themes: prognostic prediction models, radiomic diagnosis, and multi-omics integration. External validation results from PubMed showed high consistency with the main analysis; however, the clinical translation gap analysis revealed a 16:1 retrospective-validation-to-development ratio (24 retrospective validation studies versus 394 development studies) and a 44:1 prospective-trial-to-development ratio (9 registered trials versus 394 development studies). No prospective interventional trials were identified. Conclusion: Research on machine learning in the field of triple-negative breast cancer (TNBC) is currently undergoing rapid expansion, with current research hotspots centered on radiomics, deep learning, and the discovery of multi-omics biomarkers. However, clinical validation remains scarce: only 24 retrospective validation studies (6.1%) and 9 registered trials (2.3%) were identified relative to 394 computational development studies. Our bibliometric analysis reveals a substantial gap between computational publication output and clinical validation evidence, and the research paradigm remains primarily methodology-driven rather than clinically anchored. Multicenter, prospective clinical trials with patient-centered endpoints are needed to bridge this structural translation gap.