Evidence map›Paper›PMID 40265184›Full record

ArticleFrontiers in medicine2025

MRI based volumetric lung nodule assessment - a comparison to computed tomography.

Tatjana Dell, Andreas Feisst, Olga Ramig, Yannik Layer, Narine Mesropyan, Alexander Isaak, Claus Pieper, Patrick Kupczyk, Julian Luetkens, Daniel Thomas and 1 more

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Tatjana DellDepartment of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn, University Hospital Bonn, Bonn, Germany.
Andreas FeisstDepartment of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn, University Hospital Bonn, Bonn, Germany.
Olga RamigDepartment of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn, University Hospital Bonn, Bonn, Germany.
Yannik LayerDepartment of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn, University Hospital Bonn, Bonn, Germany.
Narine MesropyanDepartment of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn, University Hospital Bonn, Bonn, Germany.
Alexander IsaakDepartment of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn, University Hospital Bonn, Bonn, Germany.
Claus PieperDepartment of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn, University Hospital Bonn, Bonn, Germany.
Patrick KupczykDepartment of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn, University Hospital Bonn, Bonn, Germany.
Julian LuetkensDepartment of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn, University Hospital Bonn, Bonn, Germany.
Daniel ThomasDepartment of Diagnostic and Interventional Radiology, St. Vinzenz Hospital, Cologne, Germany.
Daniel KuettingDepartment of Diagnostic and Interventional Radiology, Quantitative Imaging Lab Bonn, University Hospital Bonn, Bonn, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Previous studies have demonstrated that nodule volumetry allows for the deduction of imaging-based biomarkers such as volume doubling time, enabling superior discrimination between benign and malignant lesions compared to 2D-based morphological characteristics. The study aimed to assess the feasibility and accuracy of Materials and methods: This study involved a subgroup analysis of 233 participants from a prospective, single-center lung cancer screening program using CT and MRI. Patients were included if foci ≥6 mm were detected in CT during the initial screening round, resulting in 23 participants with 47 pulmonary nodules. MRI was performed using a 1.5 Tesla unit with a transverse T2-weighted MultiVane XD imaging technique, while low-dose CT (LDCT) was performed on a 128-slice spiral CT scanner. Volumetric nodule assessment was conducted using a computer-aided diagnosis system, with images reviewed by two experienced radiologists. Statistical analysis included regression analysis, Bland-Altman analysis, and calculation of the interclass correlation coefficient (ICC) to assess correlation and reproducibility. Results: Comparison of MRI-based volumetric assessment with LDCT as the reference standard revealed a mean nodule volume of 1.1343 ± 3.1204 cm Conclusion: This study demonstrates that MRI-based volumetry of lung nodules ≥6 mm is feasible and accurate, showing comparable precision to CT with minimal bias in volume measurements, and highlights the potential of MRI as a radiation-free alternative for lung nodule follow-up and screening.

Indexed as

early detection of cancerlow-dose computed tomographylung cancer screeningmagnetic resonance imagingpulmonary nodulesradiation protectionvolumetry

Identifiers

PMID40265184
PMCPMC12013721

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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.