Evidence map›Paper›PMID 42022539›Full record

ReviewMedComm2026

Early Cancer Detection: What's Going on and What's Next.

Emma Di Carlo

Abstract readReview
In one paragraph

Review in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Review
  2. 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

1 author.

Emma Di CarloDepartment of Medicine and Sciences of Aging "G. d'Annunzio" University of Chieti-Pescara Chieti Italy.ORCID https://orcid.org/0000-0001-7778-1042

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Late-stage cancer diagnosis and limited treatment options for advanced disease remain major contributors to cancer-related morbidity and mortality. Blood-based multicancer early detection (MCED) assays have consequently gained momentum as a means to shift diagnosis toward earlier, more curable stages. Despite their promise, substantial methodological, clinical, and implementation barriers hinder widespread adoption. Integrative approaches coupling multi-omics profiling with advanced molecular imaging may improve detection accuracy and tumor localization, while risk-adapted MCED paradigms could support more targeted, individualized screening strategies. This article reviews the current landscape of MCED technologies, with a primary focus on circulating cell-free DNA and circulating tumor DNA-based assays, and critically evaluates their developmental status, strengths, and limitations relative to established single-cancer screening methods. The contribution of artificial intelligence, particularly advanced deep learning,  to improving sensitivity, specificity, and predictive performance is discussed. The potential of MCED assays to detect aggressive, currently unscreened malignancies and to address the unique challenges of pediatric cancers is examined. In addition, emerging alternative detection strategies, ongoing clinical validation efforts, regulatory requirements, and implementation considerations are reviewed. Finally, the impact of MCED testing on cancer mortality, quality of life, and healthcare systems is outlined, along with key technological trends shaping future development and clinical translation.

Indexed as

artificial intelligence modelscirculating cell‐free DNAearly cancer diagnosisliquid biopsymulticancer early detection teststumor‐associated biomarkers

Identifiers

PMID42022539
PMCPMC13097591

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.