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European Radiology
Erschienen in:

16.08.2024 | Chest

Artificial intelligence-driven volumetric CT outcome score in cystic fibrosis: longitudinal and multicenter validation with/without modulators treatment

verfasst von: Amel Imene Hadj Bouzid, Stephanie Bui, Ilyes Benlala, Patrick Berger, Antoine Hutt, Renan Liberge, Paul Habert, Jean-Yves Gaubert, Marie Baque-Juston, Baptiste Morel, Gilbert Ferretti, Baudouin Denis de Senneville, François Laurent, Julie Macey, Gaël Dournes

Erschienen in: European Radiology | Ausgabe 2/2025

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Abstract

Objectives

Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations.

Materials and methods

The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score.

Results

A total of 139 CF patients (median age, 15 years [interquartile range: 13–25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99.

Conclusion

NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI.

Clinical relevance statement

Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials.

Key Points

  • Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).
  • AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.
  • AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.

Graphical Abstract

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Metadaten
Titel
Artificial intelligence-driven volumetric CT outcome score in cystic fibrosis: longitudinal and multicenter validation with/without modulators treatment
verfasst von
Amel Imene Hadj Bouzid
Stephanie Bui
Ilyes Benlala
Patrick Berger
Antoine Hutt
Renan Liberge
Paul Habert
Jean-Yves Gaubert
Marie Baque-Juston
Baptiste Morel
Gilbert Ferretti
Baudouin Denis de Senneville
François Laurent
Julie Macey
Gaël Dournes
Publikationsdatum
16.08.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology / Ausgabe 2/2025
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-024-11019-5

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