Evidence mapPaperPMID 41928658Full record

ReviewJournal of magnetic resonance imaging : JMRI2026

MRI for Lung Cancer Management: Any Closer to Clinical Application?

Juergen Biederer, Liisa L Bergmann, Jeanne B Ackman, Bruno Hochhegger, Lea Azour, Simon M F Triphan, Julien Dinkel, Yoshiharu Ohno, Yoshiyuki Ozawa, Edwin J R van Beek and 1 more

Abstract readReview
In one paragraph

Review in Journal of magnetic resonance imaging : JMRI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Juergen BiedererDiagnostic and Interventional Radiology, University Hospital, Heidelberg, Germany.ORCID 0000-0003-4295-3451
Liisa L BergmannDepartment of Radiology, Cardiothoracic Imaging Section, Medical College of Wisconsin, Froedtert Hospital, Milwaukee, Wisconsin, USA.
Jeanne B AckmanDepartment of Radiology, Division of Thoracic Imaging and Intervention, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID 0000-0001-5022-9530
Bruno HochheggerDepartment of Radiology, University of Florida, Gainesville, Florida, USA.
Lea AzourDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.ORCID 0000-0002-3658-8956
Simon M F TriphanDiagnostic and Interventional Radiology, University Hospital, Heidelberg, Germany.ORCID 0000-0003-2068-1184
Julien DinkelDepartment of Radiology, University Hospital Nice, Nice, France.
Yoshiharu OhnoDepartment of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan.ORCID 0000-0002-4431-1084
Yoshiyuki OzawaJoint Research Laboratory of Advanced Medical Imaging and Artificial Intelligence, Fujita Health University School of Medicine, Toyoake, Japan.
Edwin J R van BeekInstitute for Neuroscience and Cardiovascular Research, Edinburgh Imaging, University of Edinburgh, Edinburgh, UK.ORCID 0000-0002-2777-5071
Lena WucherpfennigDiagnostic and Interventional Radiology, University Hospital, Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Management of lung cancer (LC) encompasses screening, diagnosis, staging, radiotherapy planning and guidance, therapy monitoring and surveillance. Across these domains, magnetic resonance imaging (MRI) offers a range of morphological and functional imaging capabilities-including diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) imaging, and whole-body MRI-to complement established imaging modalities. Recent technical advances have substantially improved the feasibility of lung MRI, enabling more reliable image acquisition and lesion assessment under controlled conditions. In LC screening, meta-analyses and prospective studies indicate that MRI can detect solid pulmonary nodules above clinically actionable size thresholds with moderate to high sensitivity and a low false-positive rate. However, the available evidence is largely derived from pilot studies, selected cohorts, and modeling-based analyses. MRI should therefore be regarded as technically feasible for screening but not yet a validated alternative to low-dose computed tomography in population-based programs. For staging, whole-body MRI incorporating DWI has demonstrated comparable diagnostic performance to standard multimodality pathways in prospective and randomized studies, with potential advantages including reduced radiation exposure and streamlined imaging workflows. In radiotherapy planning, DCE, DWI, and motion-resolved MRI techniques can improve target delineation and treatment adaptation, but their use remains largely confined to specialized centers. MRI shows promise for therapy response assessment and prognostication through quantitative DCE- and DWI-derived biomarkers, although reported parameters remain heterogeneous and insufficiently standardized for routine clinical decision-making. Overall, MRI has established clinical utility in selected aspects of LC management, while broader adoption is currently limited by availability, standardization, and validation gaps. Further technical refinement and large-scale prospective trials are required to define its role in routine clinical practice. LEVEL OF EVIDENCE: 5. TECHNICAL EFFICACY: Stage 2.

Indexed as

Lung NeoplasmsMagnetic Resonance ImagingContrast MediaDiffusion Magnetic Resonance ImagingDynamic Contrast Enhanced Magnetic Resonance ImagingHumansLungNeoplasm StagingReproducibility of ResultsSensitivity and SpecificityContrast Medialung cancermagnetic resonance imaging (MRI)prediction of prognosisscreeningstagingsurveillance

Identifiers

PMID41928658
PMCPMC13175227

What Socratic holds

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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.