Evidence map›Paper›PMID 42429838›Full record

ArticleEuropean radiology2026

Diagnostic performance of a single breath-hold lung MRI scan with AI-powered compressed sensing for nodule detection in comparison to photon counting detector-CT.

Anna Palmisano, Giulia Piccinni, Davide Serra, Elisa Bruno, Giulio Ferrazzi, Davide Vignale, Carlo Tacchetti, Antonio Esposito

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Anna PalmisanoAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Milan, Italy. palmisano.anna@hsr.it.ORCID http://orcid.org/0000-0002-1564-2060
Giulia PiccinniAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Milan, Italy.
Davide SerraVita-Salute San Raffaele University, Milan, Italy.
Elisa BrunoAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Milan, Italy.
Giulio FerrazziPhilips Healthcare, Milan, Italy.
Davide VignaleAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Milan, Italy.
Carlo TacchettiVita-Salute San Raffaele University, Milan, Italy.
Antonio EspositoAdvanced Imaging for Personalized Medicine Unit, Experimental Imaging Center, IRCCS San Raffaele Hospital, Milan, Italy.

Funding

Piano Nazionale di Ripresa e Resilienza PNRR "Digital Driven Diagnostics, prognostics and therapeutics for susteinable Health Care PNC 0000001
6 · The paper itself

Abstract

objectiveThe aim was to evaluate the performance of a single-breath-hold accelerated 3D-T1w-FFE 3-T MRI sequence for screening lung nodules, compared with photon-counting detector CT (PCD-CT), which served as the reference standard. MATERIALS AND

methodsIn this single-center prospective study, 148 healthy adults underwent lung 3-T MRI between June 2024 and June 2025 using a single-breath-hold, AI-aided compressed sensing (AI-CS)- accelerated 3D-T1-FFE sequence (acceleration factor of 9) to evaluate for the presence of lung nodules. Patients scanned within 24 h using PCD-CT, for comparison, were enrolled. Lung-RADS scores were evaluated per patient, and all nodules were assessed for size and composition. The diagnostic performance of MRI was compared with PCD-CT, including interobserver and intermodality agreement, using Lin's Concordance Correlation Coefficient (CCC).

results97 patients were enrolled (65 men, 67%; mean age 58 ± 12 years old) and 33 (34%) were active smokers. MRI and CT showed complete agreement in the Lung-RADS 4A and 4B categories. MRI demonstrated an overall sensitivity of 83.1% (95% CI: 73.2-89.9) for solid nodules, increasing to 98.1% (95% CI: 90.2-99.7) for nodules ≥ 4 mm. Lin's CCC for nodule size was 0.985, indicating excellent agreement, with MRI showing a slight underestimation of 1.02 mm (95% CI: [-1.16, 3.20]) in Bland-Altman analysis. MRI failed to detect calcified nodules < 4 mm, which accounted for 51.2% of undetected nodules.

conclusionsA single breath-hold AI-CS accelerated 3D-T1-FFE MRI scan can be a valuable tool for solid lung nodule screening, showing very good agreement with CT. KEY POINTS: Question May lung MRI using an optimized AI-CS accelerated 3D-T1-FFE sequence acquired in a single breath hold be a radiation-free alternative to CT in lung cancer screening? Findings Lung MRI using AI-CS 3D-T1-FFE sequence achieved an overall accuracy of 87.3% for detecting solid nodules overall, increasing to 97.7% for nodules ≥ 4 mm. Clinical relevance Lung MRI using an optimized AI-CS 3D-T1-FFE is a promising alternative to chest CT in lung nodules screening, offering high diagnostic accuracy, especially for clinically relevant nodules, excellent Lung-RADS classification agreement, and short acquisition time (17.2 s [IQR, 17.2-17.3]).

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Computed tomographyLungMRIScreening

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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.