Evidence map›Paper›PMID 40027142›Full record

ReviewThe journal of liquid biopsy2024

Liquid biopsy for monitoring minimal residual disease in localized and locally-advanced non-small cell lung cancer after radical-intent treatment.

Héctor Aguilar, Belén López-Roldán, Anna Vilalta-Lacarra, Gorka Alkorta-Aranburu, R Claramunt, José Antonio López-Guerrero, S Sandiego, I Gil-Bazo

Abstract readReview
In one paragraph

Review in The journal of liquid biopsy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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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

8 authors.

Héctor AguilarDepartment of Medical Oncology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
Belén López-RoldánDepartment of Medical Oncology, Hospital Santa Bárbara, Soria, Spain.
Anna Vilalta-LacarraDepartment of Medical Oncology, Clínica Universidad de Navarra, Pamplona, Spain.
Gorka Alkorta-AranburuCIMA LAB Diagnostics, Universidad de Navarra, Pamplona, Spain.
R ClaramuntMolecular Biology Laboratory, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
José Antonio López-GuerreroMolecular Biology Laboratory, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
S SandiegoDepartment of Medical Oncology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
I Gil-BazoDepartment of Medical Oncology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Blood-based biomarkers investigation does not require invasive tissue biopsies and may explore diverse tumoral components such as proteins, microRNAs, circulating tumor cells, ctDNA, and exosomes and may better reflect tumor molecular heterogeneity, either temporal or spatial. ctDNA is related to tumor burden and represents a more objective measure of the total body disease burden than imaging findings. ctDNA profiling can be therefore useful to determine minimal residual disease (MRD), which is defined as the remaining tumor cells or tumor-derived material after definitive treatment in patients with no clinical evidence of disease. The detection of MRD is highly predictive of future disease recurrence. Although detectable MRD is associated with a poor prognosis, it is not clear whether MRD detection can guide therapy escalation to improve patient outcomes. In this review, we present four cases of epidermal growth factor receptor (

Indexed as

ctDNAEpidermal growth factor receptorLiquid biopsyMinimal residual diseaseNSCLC

Identifiers

PMID40027142
PMCPMC11863883

What OpenQuestion holds

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Registered trials

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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.