Evidence map›Paper›PMID 39035726›Full record

ArticleJournal of the National Cancer Center2023

National Cancer Institute's early detection research network: a model organization for biomarker research.

Paul D Wagner, Sudhir Srivastava

Abstract read
In one paragraph

Article in Journal of the National Cancer Center, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Procalcitonin and interleukin- 6 in predicting prognosis of sepsis patients with cancer.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025
    Article
  5. Article
  6. Review
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

2 authors.

Paul D WagnerCancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, USA.
Sudhir SrivastavaCancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For many cancers a primary cause of poor survival is that they are detected at a late stage when therapies are less effective. Although screening methods exist to detect some types of cancer at an early stage, there are currently no effective methods to screen for most types of cancer. Biomarkers have the potential to improve detection of early-stage cancers, risk stratification, and prediction of which pre-cancerous lesions are likely to progress and to make screening tests less invasive. Although thousands of research articles on biomarkers for early detection are published every year, few of these biomarkers have been validated and shown to be clinically useful. This reflects both the inherent difficulty in detecting early-stage cancers and a disconnect between the process of discovering biomarkers and their use in the clinic. To overcome this limitation the US National Cancer Institute created the Early Detection Research Network. It is a highly collaborative program that brings together biomarker discoverers, assay developers, and clinicians. It provides an infrastructure that is essential for developing and validating biomarkers and imaging methods for early cancer detection and has successfully completed several multicenter validation studies.

Indexed as

BiomarkersCollaborationData sharingEarly detectionValidation

Identifiers

PMID39035726
PMCPMC11256707

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.