Evidence map›Paper›PMID 33161470›Full record

ArticleClinical rheumatology2021

Lung magnetic resonance imaging in systemic sclerosis: a new promising approach to evaluate pulmonary involvement and progression.

Luna Gargani, Cosimo Bruni, Daniele De Marchi, Chiara Romei, Serena Guiducci, Silvia Bellando-Randone, Giovanni Donato Aquaro, Alessia Pepe, Emanuele Neri, Stefano Colagrande and 4 more

Open access · greenAbstract read
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In one paragraph

Article in Clinical rheumatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
1.9field-weighted citation impact, top 14% of its field
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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.

  1. Longitudinal relaxation rate (Magma (New York, N.Y.) · 2025
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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

14 authors at 7 institutions in 1 country.

Luna Gargani *Institute of Clinical Physiology, National Research Council, Via Moruzzi, 1, 56124, Pisa, Italy. gargani@ifc.cnr.it.
Cosimo Bruni *Department of Experimental and Clinical Medicine, Department of Geriatric Medicine, Division of Rheumatology AOUC, University of Florence, Florence, Italy.ORCID http://orcid.org/0000-0003-2813-2083
Daniele De MarchiDepartment of Magnetic Resonance, Fondazione Toscana G. Monasterio, Pisa, Italy.
Chiara RomeiII Radiology Unit, University Hospital of Pisa, Pisa, Italy.
Serena GuiducciDepartment of Experimental and Clinical Medicine, Department of Geriatric Medicine, Division of Rheumatology AOUC, University of Florence, Florence, Italy.
Silvia Bellando-RandoneDepartment of Experimental and Clinical Medicine, Department of Geriatric Medicine, Division of Rheumatology AOUC, University of Florence, Florence, Italy.
Giovanni Donato AquaroDepartment of Magnetic Resonance, Fondazione Toscana G. Monasterio, Pisa, Italy.
Alessia PepeDepartment of Magnetic Resonance, Fondazione Toscana G. Monasterio, Pisa, Italy.
Emanuele NeriDiagnostic and Interventional Radiology, Department of Translational Research, University of Pisa, Pisa, Italy.
Stefano ColagrandeDepartment of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
Fabio FalaschiII Radiology Unit, University Hospital of Pisa, Pisa, Italy.
Alberto Moggi-PignoneDepartment of Experimental and Clinical Medicine, Department of Emergency Medicine DEA, Division of Internal Medicine AOUC, University of Florence, Florence, Italy.
Alessandro PingitoreInstitute of Clinical Physiology, National Research Council, Via Moruzzi, 1, 56124, Pisa, Italy.
Marco Matucci-CerinicDepartment of Experimental and Clinical Medicine, Department of Geriatric Medicine, Division of Rheumatology AOUC, University of Florence, Florence, Italy.
University of Florence · ITFondazione Toscana Gabriele Monasterio · ITAzienda Ospedaliera Universitaria Pisana · ITAzienda Ospedaliero-Universitaria Careggi · ITIstituto di Fisiologia Clinica · ITIstituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" · ITUniversity of Pisa · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

INTRODUCTION/

objectivesInterstitial lung disease (ILD) is frequent and highly disabling in systemic sclerosis (SSc). Magnetic resonance imaging (MRI) is not routinely used to evaluate the lung, due to poorer spatial resolution compared to high-resolution computed tomography (HRCT). We aimed to compare lung MRI signal with HRCT and evaluate the role of MRI in predicting ILD progression.

methodsThirty SSc patients underwent lung MRI and HRCT. STIR and T1 mapping sequences were acquired before and after gadolinium injection. Patients were classified as normal (group 1 with normal HRCT and MRI), discordant (group 2 without ILD signs on HRCT but areas of hyperintensity on MRI), and abnormal (group 3 with ILD signs on HRCT and areas of hyperintensity on MRI). Patients were followed up for ILD progression.

resultsMean STIR and T1 values were different between the three groups (p < 0.0001). STIR values correlated with HRCT score (R = 0.79, p < 0.0001), lung ultrasound B-lines (R = 0.73, p < 0.0001), and %DLco (R = - 0.63, p = 0.0001). Nine events were recorded during a follow-up of 25 ± 20 months. Continuous STIR values were independently associated with events (HR 1.018; CI 1.005-1.031, p = 0.005). A STIR value >90 ms discriminated patients at a higher risk of worsening pulmonary involvement (HR 8.80; CI 1.81-42.74; p < 0.007).

conclusionsLung MRI can detect SSc-related ILD, with good correlations with other ILD markers. STIR values, independently of HRCT appearance, may predict worsening lung involvement. Lung MRI, although very preliminary, is a promising tool that in a near future could help selecting patients for an early treatment of SSc-related ILD and a more appropriate use of HRCT. Key points • Lung MRI has the potential to differentiate inflammation-predominant versus fibrosis-predominant lesions, but it is not currently used in routine clinical practice to assess SSc-related ILD. • Lung MRI STIR and T1 values are significantly different between patients with and without SSc-related ILD. STIR values, independently of HRCT appearance, are also able to predict worsening lung involvement over time. • These preliminary data suggest that, in a near future, MRI could support the choice for an early treatment of SSc-related ILD, as well as a more appropriate use of HRCT.

Indexed as

Lung Diseases, InterstitialScleroderma, SystemicHumansLungMagnetic Resonance ImagingRespiratory Function TestsInterstitial lung diseaseMagnetic resonance imagingSystemic sclerosis

Identifiers

PMID33161470
OpenAlexW3097193600

What OpenQuestion holds

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Read underepoch 390

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.