Evidence map›Paper›PMID 39975740›Full record

ReviewJournal of thoracic disease2025

Improving diagnostic capabilities in lung cancer through next-generation sequencing: a narrative review.

Andrew Figueroa, Shadi Safar Gholi, Girish Jayant, Raj Wadgaonkar, Ahmed Gubran, Stephen J Kuperberg

Abstract readReview
In one paragraph

Review in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Andrew FigueroaDepartment of Pulmonary Medicine, Doylestown Hospital, Ambler, PA, USA.
Shadi Safar GholiDepartment of Internal Medicine, New York City Health and Hospitals, New York, NY, USA.ORCID https://orcid.org/0009-0005-4003-7101
Girish JayantDepartment of Internal Medicine, Montefiore Medicine Center, The Bronx, NY, USA.ORCID https://orcid.org/0000-0001-6037-7852
Raj WadgaonkarCollege of Medicine, SUNY Downstate Medical Center, Brooklyn, NY, USA.ORCID https://orcid.org/0009-0008-5121-6414
Ahmed GubranDepartment of Internal Medicine, New York City Health and Hospitals, New York, NY, USA.
Stephen J KuperbergDepartment of Internal Medicine, New York City Health and Hospitals, New York, NY, USA.ORCID https://orcid.org/0000-0002-6819-4425

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: Lung neoplasia is the leading cause of cancer death worldwide, thus, early detection and accuracy in establishing a diagnosis is paramount. As a consequence of decades of basic and translational studies revealing the genetic basis of lung cancer, a paradigm shift has occurred toward a personalized approach to medicine whereby mutational analysis confers an opportunity for safer, and expedient treatment options. In this context, next-generation sequencing (NGS) has emerged as a vital technological advance, and has become increasingly established as a core method for rapidly and effectively identifying actionable mutations in lung cancer. For these reasons, an updated review of the literature across invasive and non-invasive diagnostic modalities in lung cancer is warranted to inform diagnostic approaches and prompt new investigations. The objective of the present review is to provide a focused update on applications of NGS in lung cancer diagnostics, with a special focus on tissue acquisition methodologies and mutational analysis. Methods: The search strategy included a survey of the current literature from 2005 to 2024 in PubMed, Medline, Scopus, and Google Scholar. Eligible study types included original research, literature reviews (narrative and systematic), and observational studies. which encompassed findings pertinent to the lung cancer diagnostics, mutational analysis and lung cancer treatment overlapping with applications and use of NGS technologies. Key Content and Findings: There are extensive and diverse advantages to the use of NGS in lung cancer diagnostics, especially when compared to traditional sequencing techniques including, speed, effectiveness, easy adoption in the context of analysis of samples prepared for lung cancer diagnosis. Advances in cell-free DNA reinforce the firm role of NGS in novel approaches. Conclusions: NGS implementation is a crucial and beneficial technological leap in lung cancer diagnosis, especially given the environment of novel and established targeted and immune based therapies which require mutational testing. Its numerous benefits such as expedient results and reduced sample requirements will continue to ability optimize lung cancer outcomes by virtue of improved patient safety, reduction of unnecessary procedures, and provision of accurate results.

Indexed as

bronchoscopygenomic profilingimage guided needle aspirationLung cancerlung tissue acquisition

Identifiers

PMID39975740
PMCPMC11833572

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.