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Background <p>Medical 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.</p> Main body <p>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.</p> Conclusion <p>Overall, imaging-driven, integrated preclinical investigation strategies represent a powerful and ethically responsible approach to refining disease modeling and accelerating drug development in pulmonary medicine.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月22日

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

摘要

Background

Medical 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.

Conclusion

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