Evidence map›Paper›PMID 35774426›Full record

ReviewChronic diseases and translational medicine2022

Research Progress on Postoperative Minimal/Molecular Residual Disease Detection in Lung Cancer.

Manqi Wu, Haifeng Shen, Ziyang Wang, Nnennaya Kanu, Kezhong Chen

Abstract readReview
In one paragraph

Review in Chronic diseases and translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

5 authors.

Manqi WuDepartment of Thoracic Surgery Peking University People's Hospital, Peking University Beijing 100044 China.
Haifeng ShenDepartment of Thoracic Surgery Peking University People's Hospital, Peking University Beijing 100044 China.
Ziyang WangDepartment of Thoracic Surgery Peking University People's Hospital, Peking University Beijing 100044 China.
Nnennaya KanuCancer Research UK Lung Cancer Centre of Excellence University College London Cancer Institute, University College London 72 Huntley St London WC1E 6DD UK.
Kezhong ChenDepartment of Thoracic Surgery Peking University People's Hospital, Peking University Beijing 100044 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide. Approximately 10%-50% of patients experience relapse after radical surgery, which may be attributed to the persistence of minimal/molecular residual disease (MRD). Circulating tumor DNA (ctDNA), a common liquid biopsy approach, has been demonstrated to have significant clinical merit. In this study, we review the evidence supporting the use of ctDNA for MRD detection and discuss the potential clinical applications of postoperative MRD detection, including monitoring recurrence, guiding adjuvant treatment, and driving clinical trials in lung cancer. We will also discuss the problems that prevent the routine application of ctDNA MRD detection. Multi-analyte methods and identification of specific genetic and molecular alterations, especially methylation, are effective detection strategies and show considerable prospects for future development. Interventional prospective studies based on ctDNA detection are needed to determine whether the application of postoperative MRD detection can improve the clinical outcomes of lung cancer patients, and the accuracy, sensitivity, specificity, and robustness of different detection methods still require optimization and refinement.

Indexed as

circulating tumor DNAliquid biopsylung cancerminimal/molecular residual disease

Identifiers

PMID35774426
PMCPMC9215711

What OpenQuestion 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 OpenQuestion graph.