Evidence map›Paper›PMID 40655938›Full record

ArticleOncoTargets and therapy2025

Biomarker and Prognostic Value of Super-ARMS Detection for EGFR Mutation in Advanced NSCLC.

Huicong Liu, Hui Li, Lisha Xiao, Yubiao Guo, Gengpeng Lin

Abstract read
In one paragraph

Article in OncoTargets and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Huicong Liu *Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Institute of Pulmonary Diseases, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China.
Hui Li *Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China.
Lisha XiaoDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Institute of Pulmonary Diseases, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China.
Yubiao GuoDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Institute of Pulmonary Diseases, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China.
Gengpeng LinDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Institute of Pulmonary Diseases, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: ctDNA is a non-invasive and convenient method for detecting EGFR mutations in non-small cell lung cancer (NSCLC). However, its sensitivity is lower than that of tissue-based testing. To enhance ctDNA detection efficiency, we identified the patient population most suitable for ctDNA testing, assessed the relationship between ctDNA and tumor markers, and examined the clinical significance of ctDNA in medical practice. Methods: A single-center retrospective study was conducted, including 135 patients with NSCLC who underwent histological and liquid Super-ARMS tests. Of these, 92 patients with EGFR mutations detected in both tumor tissue and plasma were classified into the EGFR Results: The positivity rate of Super-ARMS test was 68.1% (92/135). The presence of EGFR Conclusion: Patients exhibiting metastasis, elevated levels of tumor markers and SUV

Indexed as

CEActDNAEGFR mutationNSCLCprognosis

Identifiers

PMID40655938
PMCPMC12255349

What Socratic holds

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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.