Abstract
Rationale Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis airway structural disease in vivo. However, visual scorings as an outcome measure are time-consuming, require training, and lack high reproducibility.
Objective To validate a fully automated artificial intelligence-driven scoring of cystic fibrosis lung disease severity.
Methods Data were retrospectively collected in three cystic fibrosis reference centers, between 2008 and 2020, in 184 patients 4 to 54-years-old. An algorithm using three two-dimensional convolutional neural networks was trained with 78 patients’ CTs (23 530 CT slices) for the semantic labeling of bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, and collapse/consolidation. 36 patients’ CTs (11 435 CT slices) were used for testing versus ground-truth labels. The method's clinical validity was assessed in an independent group of 70 patients with or without lumacaftor/ivacaftor treatment (n=10 and 60, respectively) with repeat examinations. Similarity and reproducibility were assessed using Dice coefficient, correlations using Spearman test, and paired comparisons using Wilcoxon rank test.
Measurement and main results The overall pixelwise similarity of artificial intelligence-driven versus ground-truth labels was good (Dice coefficient=0.71). All artificial intelligence-driven volumetric quantifications had moderate to very good correlations to a visual imaging scoring (p<0.001) and fair to good correlations to FEV1% at pulmonary function test (p<0.001). Significant decreases in peribronchial thickening (p=0.005), bronchial mucus (p=0.005), bronchiolar mucus (p=0.007) volumes were measured in patients with lumacaftor/ivacaftor. Conversely, bronchiectasis (p=0.002) and peribronchial thickening (p=0.008) volumes increased in patients without lumacaftor/ivacaftor. The reproducibility was almost perfect (Dice>0.99).
Conclusion Artificial intelligence allows a fully automated volumetric quantification of cystic fibrosis-related modifications over an entire lung. The novel scoring system could provide a robust disease outcome in the era of effective CFTR modulator therapy.
Footnotes
This manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.
Conflict of interest: G. Dournes reports an academic grant to spend a research program in the USA from the French Society of Radiology and IdEx Bordeaux, for the submitted work; lecture payments from Margaux Orange, outside the submitted work.
Conflict of interest: C. Hall reports grants from Boehringer-Ingelheim; lecture payment or honoraria from Boehringer-Ingelheim and VIDA Diagnostics, outside the submitted work.
Conflict of interest: Matthew M. Willmering has nothing to disclose.
Conflict of interest: Alan S. Brody has nothing to disclose.
Conflict of interest: Julie Macey has nothing to disclose.
Conflict of interest: Stephanie Bui has nothing to disclose.
Conflict of interest: Baudouin Denis-De-Senneville has nothing to disclose.
Conflict of interest: Patrick Berger has nothing to disclose.
Conflict of interest: F. Laurent reports technical support to conduct lung magnetic resonance imaging research in cystic fibrosis from Siemens Healthineers, outside the submitted work.
Conflict of interest: Ilyes Benlala has nothing to disclose.
Conflict of interest: J. Woods reports investigator-initiated support and consulting fees from Vertex Pharmaceuticals, outside the submitted work.
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- Received March 22, 2021.
- Accepted July 2, 2021.
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