Evidence map›Paper›PMID 41572273›Full record

ReviewRespiratory research2026

Multidimensional lung imaging: integrated preclinical platforms enabling the identification of translational biomarkers for pulmonary research.

Francesca Pennati, Martina Buccardi, Andrea Aliverti, Erica Ferrini, Franco Fabio Stellari

Abstract readReview
In one paragraph

Review in Respiratory research, 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

5 authors.

Francesca PennatiDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.
Martina BuccardiExperimental Pharmacology and Translational Science Department, Molecular Imaging Facility, Chiesi Farmaceutici S.p.A., Corporate Pre-Clinical R&D , Largo Belloli 11/A, Parma, 43122, Italy.
Andrea AlivertiDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.
Erica FerriniExperimental Pharmacology and Translational Science Department, Molecular Imaging Facility, Chiesi Farmaceutici S.p.A., Corporate Pre-Clinical R&D , Largo Belloli 11/A, Parma, 43122, Italy.
Franco Fabio StellariExperimental Pharmacology and Translational Science Department, Molecular Imaging Facility, Chiesi Farmaceutici S.p.A., Corporate Pre-Clinical R&D , Largo Belloli 11/A, Parma, 43122, Italy. fb.stellari@chiesi.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedical imaging is changing diagnostics, elucidating molecular disease mechanisms, supporting patient stratification, and advancing drug development toward personalized medicine across multiple therapeutic areas. Regrettably, in respiratory research, it is rarely used as a primary endpoint in clinical trials, despite the pressing need for non-invasive biomarkers, particularly in pulmonary disease, where anatomical complexity and patient risk often preclude lung biopsies. MAIN BODY: This review describes how biomarkers derived from preclinical imaging, when combined with end-stage readouts such as OMIC data within multi-integrative platforms, can provide a comprehensive and multiscale understanding of lung pathology. Focusing on rodent models, we survey a range of imaging modalities, including anatomical (micro-CT, MRI), optical (BLI, FLI), and functional (PET, SPECT), emphasizing their role in longitudinal in vivo studies. These approaches are complemented by end-stage bioanalytical tools, such as histology, tissue clearing, and spatial omics, implemented within scalable workflows. The feasibility and translational aspects of these technologies, including considerations related to dose, operational requirements, and emerging needs for protocol standardization, are also examined, as these factors critically influence data robustness and reproducibility. A key component of these multi-level platforms is the systematic matching and integration of in vivo imaging with end-stage data, enabling quantitative pathology validation, the acquisition of etiopathological insights, as well as biomarker discovery. These multilayered platforms also take advantage of advanced computational tools, including machine learning and explainable AI, which improve interpretability, reproducibility, and translational relevance of the data in the context of personalized medicine. These strategies further strengthen early disease assessment, improving diagnostic precision and informing therapeutic development.

conclusionOverall, imaging-driven, integrated preclinical investigation strategies represent a powerful and ethically responsible approach to refining disease modeling and accelerating drug development in pulmonary medicine.

Indexed as

Diagnostic ImagingLungLung DiseasesTranslational Research, BiomedicalAnimalsBiomarkersHumansBiomarkersFunctional imagingMicro-computed tomography (micro-CT)Multi-omics integrationPreclinical imagingPulmonary disease modelsTranslational researchVolumetric histology

Identifiers

PMID41572273
PMCPMC12911040

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.