Evidence map›Paper›PMID 35440720›Full record

ReviewNature medicine2022

The future of early cancer detection.

Rebecca C Fitzgerald, Antonis C Antoniou, Ljiljana Fruk, Nitzan Rosenfeld

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 179 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
179citing papers in PubMed, 5 pooled it
54.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

179 citing papers in PubMed, 5 syntheses or guidelines pooled it, 411 citations in OpenAlex.

  1. Pooled it
  2. Diagnostic performance of [Frontiers in medicine · 2025
    Pooled it
  3. Pooled it
  4. Pooled it
  5. Head-to-head comparison ofFrontiers in oncology · 2023
    Pooled it
  6. Trial
  7. Article
  8. Exercise therapy in cancer prevention and treatment.Nature reviews. Clinical oncology · 2026
    Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Review
  18. Plasma proteomic signatures improve risk stratification and personalized screening for gastric cancer.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026
    Article
  19. Patient-derived organoids in gynaecological cancers: emerging tools for therapy and disease modelling.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
  20. Article

119 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Rebecca C FitzgeraldEarly Detection Programme, Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK. rcf29@cam.ac.uk.ORCID http://orcid.org/0000-0002-3434-3568
Antonis C AntoniouCentre for Cancer Genetic Epidemiology, Department of Public Health & Primary Care, University of Cambridge, Cambridge, UK.
Ljiljana FrukDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
Nitzan RosenfeldCancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.ORCID http://orcid.org/0000-0002-2825-4788
University of Cambridge · GB

Funding

Cancer Research UK 11906Cancer Research UK 20240Cancer Research UK 26886Cancer Research UK 27548Cancer Research UK 28292
6 · The paper itself

Abstract

A proactive approach to detecting cancer at an early stage can make treatments more effective, with fewer side effects and improved long-term survival. However, as detection methods become increasingly sensitive, it can be difficult to distinguish inconsequential changes from lesions that will lead to life-threatening cancer. Progress relies on a detailed understanding of individualized risk, clear delineation of cancer development stages, a range of testing methods with optimal performance characteristics, and robust evaluation of the implications for individuals and society. In the future, advances in sensors, contrast agents, molecular methods, and artificial intelligence will help detect cancer-specific signals in real time. To reduce the burden of cancer on society, risk-based detection and prevention needs to be cost effective and widely accessible.

Indexed as

Artificial IntelligenceNeoplasmsEarly Detection of CancerHumansNeoplasm Staging

Identifiers

PMID35440720
OpenAlexW4224215508

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.