background
In recent years, there has been significant advances in lung cancer screening, diagnosis, and the treatment of continuous development (ML) in machine learning.
method
Since 2004, we have conducted a comprehensive bibliographic analysis of 1,826 academic papers obtained from the Web of Science Core Collection to systematically explore the evolution of ML and core drive factors in lung cancer research.
result
The study reveals that the United States is at the forefront of applying ML to lung cancer research. Institutional analysis shows that Harvard University plays an important role as a major institution in this field. In the author's co-occurrence network analysis, Madhabusianant stood out as a key contributor to the application of ML in lung cancer research. Furthermore, according to journal cooccurrence analysis, Sci Rep-uk We have published the highest amount of papers in this field. It is worth noting that it includes several well-known medical journals New Engl J Med, Natureand Ca-Cancer J Clinshows great interest in this field of research. Burst citation analysis of keywords and references shows that research hotspots focused on “breast cancer” and “radiotherapy” (2004–2012) from early attention to “computer-assisted diagnosis” (2013–2017). Since 2018, “texture analysis,” “computer-aided detection,” “survival prediction,” and “radioactive” have emerged as new research trends.
Conclusion
As ML continues to be applied more broadly and deeply in lung cancer, “computer-aided detection”, “survival prediction” and “radioactive” are emerging as key areas, and it is worthy of more attention from researchers.
