ArticleDiscover oncology2026
The evolution of real-world evidence in lung cancer: a bibliometric analysis from 2009 to 2024.
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
purposeReal-world evidence (RWE) plays an increasingly important role in complementing randomized controlled trials (RCTs) in lung cancer research. This study aims to map the knowledge structure, identify research trends, and highlight future directions of real-world studies (RWS) in lung cancer through bibliometric analysis.
methodsWe retrieved publications on RWS in lung cancer from the Web of Science Core Collection (WOSCC) spanning 2009 to 2024. Bibliometric analyses were performed using CiteSpace, VOSviewer, and the R package Bibliometrix to examine publication trends, collaborative networks, co-citation patterns, and keyword bursts.
resultsA total of 2,137 articles were included. Annual publications increased markedly after 2017, accounting for 97.52% of the total. China contributed the most publications (n = 660), while the USA exhibited the highest centrality in international collaboration. Keyword burst detection identified “combination therapy,” “KRAS mutation,” and “deep learning” as the most current research frontiers.
conclusionsThis analysis underscores the pivotal role of RWE in complementing RCTs and validating therapeutic strategies in oncology. Current frontiers include combination therapy, KRAS mutations, and deep learning, indicating a shift toward precision oncology and advanced data analytics.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.