Evidence map›Paper›PMID 38068296›Full record

ReviewJournal of clinical medicine2023

The Early Diagnosis of Lung Cancer: Critical Gaps in the Discovery of Biomarkers.

Roberto Gasparri, Angela Sabalic, Lorenzo Spaggiari

Abstract readReview
In one paragraph

Review in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Smart nanoplatforms for early detection and immune modulation in lung cancer.Frontiers in bioengineering and biotechnology · 2025
    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

3 authors.

Roberto GasparriDivision of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy.ORCID 0000-0001-7922-7061
Angela SabalicDivision of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy.ORCID 0000-0002-9352-6161
Lorenzo SpaggiariDivision of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide. The main issue is the absence of a screening test available in clinical practice; the identification of noninvasive biomarkers is thus an urgent clinical necessity. Currently, low-dose computed tomography (LD-CT) demonstrates a 20% reduction in lung cancer mortality. However, it is not particularly suitable for clinical practice because of its costs, radiation, and false-positive rate. Several studies have therefore focused on research into biomarkers in body fluids. Despite the power of certain molecules to distinguish lung cancer patients from healthy subjects, no biomarker has yet been shown to significantly and reliably influence clinical decisions or to be translated from the laboratory to clinical practice. In this paper, we provide an overview of the peer-reviewed biomedical literature published in the last 10 years on the research regarding biomarkers for the early diagnosis of lung cancer via a comprehensive analysis of the reviews published this past year. Our main objective is to highlight the limitations and strengths of studies on predictive lung cancer biomarkers to stimulate further investigation for early diagnosis. Finally, we discuss future perspectives on managing clinical trials for biomarker research and their integration into clinical practice.

Indexed as

biomarkersbody fluidsearly diagnosislung cancerresearch management

Identifiers

PMID38068296
PMCPMC10707119

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.