Overview

PNEUMO-LIBS is a multicenter, non-interventional observational study designed to evaluate whether multi-element tissue imaging by Laser-Induced Breakdown Spectroscopy (LIBS), combined with an artificial intelligence-based analysis tool called Interesting Features Finder (IFF), may help physicians better understand the possible causes of chronic lung diseases.

The study focuses on adult patients with chronic lung diseases for which a lung biopsy has already been performed as part of routine medical care. These diseases include diffuse interstitial lung diseases, pulmonary granulomatoses such as sarcoidosis, and emphysema, especially when an environmental or occupational exposure is suspected but not clearly demonstrated.

Some chronic lung diseases may be influenced by inhaled mineral or metallic particles, such as silica, aluminum, titanium, or other metals. However, these exposures are often difficult to document at the individual patient level. Standard clinical, radiological, and pathological investigations do not usually provide direct information on the presence and distribution of such elements within lung tissue.

LIBS is an imaging technique that can detect and map chemical elements directly in tissue samples, including archived formalin-fixed paraffin-embedded biopsy blocks. In this study, lung biopsy samples will be analyzed with LIBS to search for elemental signatures that may be compatible with occupational or environmental exposures. The IFF algorithm will then be used to help interpret the LIBS data and identify rare or unexpected elemental signals.

The study does not require any additional biopsy, blood test, imaging examination, treatment, or hospital visit for participants. It uses tissue samples and medical data already collected during routine care. The results of LIBS and IFF analyses will be presented to the treating physician or investigator, who will complete standardized online questionnaires at three time points: before receiving the LIBS results, after receiving the LIBS report, and after receiving the IFF-assisted interpretation.

The main objective is to assess the perceived clinical utility of LIBS imaging for physicians, particularly regarding etiological understanding, possible occupational disease recognition, and prevention-oriented reasoning. Secondary objectives include assessing the added value of the IFF algorithm and evaluating the feasibility of a centralized multicenter workflow for sample transfer, LIBS analysis, IFF processing, result reporting, and questionnaire completion.

The study aims to include approximately 70 patients across several French hospital centers. Its results may support the development of future diagnostic, occupational health, and environmental medicine approaches, without modifying the medical care of participants during the study.

Eligibility

Inclusion Criteria:

• Adult patient aged 18 years or older.
• Diagnosis or documented clinical suspicion of chronic lung disease, including interstitial lung disease, pulmonary granulomatosis such as sarcoidosis, or pulmonary emphysema.
• Availability of a lung tissue biopsy sample obtained during routine medical care and stored as a formalin-fixed paraffin-embedded sample.
• Sufficient medical record information to document the clinical context, occupational or environmental exposure history, smoking status, imaging, pathology reports, and other relevant routine-care data.
• No documented objection to the secondary use of health data and biological samples for this research.

Exclusion Criteria:

• Documented objection to participation in the study or to the secondary use of health data or biological samples for research.
• Missing, unavailable, degraded, or technically unusable biopsy sample.
• Insufficient medical record information to document the clinical context or exposure history.
• Lung condition outside the scope of the study, such as isolated acute infection, isolated cardiac disease, or primary lung cancer.
• Patient under legal protection when the applicable legal conditions for non-opposition cannot be fulfilled.