Evidence map›Paper›PMID 40641909›Full record

ReviewBJR open2025

A diagnostic approach to mediastinal masses in clinical practice.

Rebecca Mura, Svitlana Pochepnia, Daria Kifjak, Natallia Khenkina, Helmut Prosch

Abstract readReview
In one paragraph

Review in BJR open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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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.

Rebecca MuraDepartment of Medicine and Surgery (diMeC), University of Parma, Parma 43125, Italy.ORCID https://orcid.org/0009-0002-0240-1142
Svitlana PochepniaChristian Doppler Laboratory for Machine Learning Driven Precision Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna 1090, Austria.
Daria KifjakChristian Doppler Laboratory for Machine Learning Driven Precision Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna 1090, Austria.
Natallia KhenkinaPostgraduation School in Radiodiagnostics, University of Milan, Milan 20122, Italy.
Helmut ProschChristian Doppler Laboratory for Machine Learning Driven Precision Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna 1090, Austria.ORCID https://orcid.org/0000-0002-6119-6364

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mediastinal masses represent a heterogeneous group of entities characterized by a variety of histopathological and radiological features. Imaging plays a pivotal role in the detection and interpretation of mediastinal abnormalities. CT remains the modality of choice due to its high spatial and temporal resolution and its ability to assess tissue composition, including the detection of fluid, fat, and calcifications. MRI represents a complementary tool in specific scenarios, such as differentiating complicated cysts from solid lesions or identifying intracellular fat content, as seen in thymic hyperplasia. The differential diagnosis of mediastinal masses relies primarily on the location of the mass and tissue composition, integrated with clinical characteristics of the patient. This review discusses the most common mediastinal masses in adults, providing a practical approach to their differentiation mainly based on the predominant density pattern and location.

Indexed as

CTmediastinal tumoursMRI

Identifiers

PMID40641909
PMCPMC12245403

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.