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Vol. 1 No. 1 (2025): December

Blibiometric analysis of detection lung cancer

https://doi.org/10.35912/aiml.v1i1.3776
25 Dec 2025

Abstract

Purpose: This study aims to analyze global research trends in lung cancer detection using a bibliometric approach. It focuses on identifying publication growth, dominant research themes, citation patterns, and collaboration networks to better understand the direction and innovation of lung cancer detection research.

Methods: A bibliometric analysis was conducted using publication records retrieved from the Scopus database covering the period from 2019 to 2024. Key indicators such as publication output, citation counts, keyword co-occurrence, and author collaboration networks were analyzed.

Results: The results indicate a steady increase in publications related to lung cancer detection over the analyzed period. Major research themes include circulating tumor DNA, early detection strategies, next-generation sequencing, and liquid biopsy technologies. The analysis also reveals strong international collaboration networks, highlighting the global nature of lung cancer research and the collective effort to improve detection technologies.

Conclusion: The study concludes that research on lung cancer detection is rapidly expanding, driven by technological advancements and growing interest in non-invasive diagnostic approaches. Emerging technologies are expected to play a critical role in enhancing early diagnosis and reducing lung cancer mortality rates.

Limitation: This study is limited by its reliance on a single database (Scopus) and a relatively short time frame, which may not capture all relevant publications or long-term research trends.

Contribution: This research provides a comprehensive baseline reference for scholars and practitioners, offering valuable insights into current research directions and supporting future advancements in early lung cancer detection methods.

Keywords

Bibliometric Analysis Circulating Tumor DNA Early Detection Lung Cancer Research Trends

How to Cite

Ahludzikri, F., Hasibuan, M., Aziz, R. A., & Triloka, J. (2025). Blibiometric analysis of detection lung cancer. Applied AI and Machine Learning Journal, 1(1), 61–71. https://doi.org/10.35912/aiml.v1i1.3776

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Authors

1 . Fiqqi Ahludzikri
Institut Informatika dan Bisnis Darmajaya, Lampung, Indonesia
2 . MS. Hasibuan
Institut Informatika dan Bisnis Darmajaya, Lampung, Indonesia
3 . RZ Abdul Aziz
Institut Informatika dan Bisnis Darmajaya, Lampung, Indonesia
4 . Joko Triloka
Institut Informatika dan Bisnis Darmajaya, Lampung, Indonesia

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Vol. 1 No. 1 (2025): December
Section: Articles
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