Evidence map›Paper›PMID 41409629›Full record

ArticleBreathe (Sheffield, England)2025

Exploring pleural effusion characterisation with quantitative thoracic ultrasound imaging: a viewpoint on the investigational role of pixel-based echogenicity analysis in transudate and exudate differentiation.

Guido Marchi

Abstract read
In one paragraph

Article in Breathe (Sheffield, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Pulmonary infections.Breathe (Sheffield, England) · 2026
    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

1 author.

Guido MarchiPulmonology Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, Pisa, Italy.ORCID https://orcid.org/0009-0000-6122-7792

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pleural effusion represents a frequent and diagnostically challenging condition across multiple clinical settings. Conventional characterisation relies on invasive thoracentesis and biochemical analysis, with Light's criteria offering high sensitivity but limited specificity. Procedural risks and patient factors may limit fluid sampling, highlighting the need for complementary, noninvasive approaches. Recent advances in thoracic ultrasound (TUS) suggest that quantitative assessment of pleural fluid (PF) echogenicity, particularly pixel density analysis and hypoechogenicity index, may represent an investigational approach for objective and reproducible evaluation. Preliminary studies using standardised image analysis platforms, such as ImageJ, indicate that exudative effusions more frequently exhibit higher pixel density than transudates, in association with markers of cellularity, protein content, and inflammation. Hybrid scoring systems combining quantitative metrics with morphological sonographic features, such as septations, fibrin strands, and debris, have been explored and may enhance diagnostic specificity, although validation remains limited. However, methodological constraints, operator- and device-dependent variability and small sample sizes continue to restrict their applicability in routine clinical practice. Emerging approaches, including artificial intelligence, may be able to mitigate these limitations by standardising measurements, harmonising grayscale output, and integrating clinical, laboratory and imaging data to generate real-time risk scores. In the absence of robustly validated evidence, quantitative TUS should be regarded as an investigational adjunct, with potential to refine PF characterisation, support clinical decision-making, and inform future research on noninvasive diagnostic strategies.

Identifiers

PMID41409629
PMCPMC12706588

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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.