A bibliometric analysis of immuno-oncology research: Is a new global leader emerging?
Bibliographic record
Abstract
e13593 Background: The last decade has witnessed an increase in cancer research globally, and a transformation of the anti-cancer armamentarium. The biggest innovation has been development of immuno-oncology (IO) therapies, and specifically immune checkpoint inhibitors (ICIs), targeting CTLA-4 and PD-(L)1, reshaping treatment paradigms for many cancers (e.g., melanoma). Since the first ICI approval in 2011, research from countries in the European Union (EU) and North America has dominated this field. However, more recently research authored in China has emerged as a potential leader. In this study we analysed cancer and IO research outputs since 2011, exploring emerging national trends, and focusing on ICI development. Methods: IO research articles were identified using a high-resolution bibliometric method, previously validated. Relevant articles were identified from the Web of Science, utilising a complex search strategy of pre-defined keywords, including the 9 FDA-approved ICI and 8 ICI approved in China-only. Additional search filters were used to determine secondary characteristics including country (assigned a fractional count based on proportional authorship), collaborations, and tumour site. Results: Since 2011, over 175,000 cancer research articles have been published globally. The number of articles published annually has increased steadily, largely driven by increasing outputs from Chinese authors. China is now the leading global publisher of cancer research accounting for 18% of all outputs in 2021, and has consistently surpassed US and EU total outputs since 2019. Over the past decade there has been a steady increase in total IO research outputs, rising by 378% (n = 2926) compared to 2011. China began publishing significant IO research in 2014 and has risen rapidly to dominate this field. In 2021, China accounted for 37% of total IO outputs. The US was previously dominant, however there has been a steady decline in proportional outputs from 63% in 2012-12 to 29% in 2019-21. Only 17% of published Chinese IO research is internationally collaborative. Further, Chinese research focuses on tumour sites with a high national prevalence, such as oesophagus, stomach and liver, dominating research outputs in these areas. Research on the 9 FDA-approved ICI is dominated by the US, with only 7% having Chinese author contribution. Conversely, the 8 ICIs approved in China-only have negligible (< 5%) author contributions from the US and EU. Conclusions: The past decade has witnessed a substantial rise in both cancer and IO research. IO articles published by Chinese authors, frequently exclusively, now account for over a third of annual outputs. Those authored in China focus on cancers with a high national prevalence, and link to ICIs approved exclusively in China. With increasing global appetite for ICIs, especially from middle-income countries, ICIs developed in China may offer a viable alternative to current options.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.191 | 0.282 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".