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Automatic analysis of bronchus-artery dimensions to diagnose and monitor airways disease in cystic fibrosis.
Lv, Qianting; Gallardo-Estrella, Leticia; Andrinopoulou, Eleni-Rosalina; Chen, Yuxin; Charbonnier, Jean-Paul; Sandvik, Rikke Mulvad; Caudri, Daan; Nielsen, Kim Gjerum; de Bruijne, Marleen; Ciet, Pierluigi; Tiddens, Harm.
Afiliação
  • Lv Q; Department of Paediatric Pulmonology and Allergology, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
  • Gallardo-Estrella L; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
  • Andrinopoulou ER; Thirona, Nijmegen, The Netherlands.
  • Chen Y; Department of Biostatistics and Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Charbonnier JP; Department of Paediatric Pulmonology and Allergology, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
  • Sandvik RM; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
  • Caudri D; Thirona, Nijmegen, The Netherlands.
  • Nielsen KG; CF Center Copenhagen, Paediatric Pulmonary Service, Department of Paediatric and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
  • de Bruijne M; University of Copenhagen, Graduate School of Health and Medical Sciences, Copenhagen, Denmark.
  • Ciet P; Department of Paediatric Pulmonology and Allergology, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
  • Tiddens H; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
Thorax ; 79(1): 13-22, 2023 12 15.
Article em En | MEDLINE | ID: mdl-37734952
ABSTRACT

BACKGROUND:

Cystic fibrosis (CF) lung disease is characterised by progressive airway wall thickening and widening. We aimed to validate an artificial intelligence-based algorithm to assess dimensions of all visible bronchus-artery (BA) pairs on chest CT scans from patients with CF.

METHODS:

The algorithm fully automatically segments the bronchial tree; identifies bronchial generations; matches bronchi with the adjacent arteries; measures for each BA-pair bronchial outer diameter (Bout), bronchial lumen diameter (Bin), bronchial wall thickness (Bwt) and adjacent artery diameter (A); and computes Bout/A, Bin/A and Bwt/A for each BA pair from the segmental bronchi to the last visible generation. Three datasets were used to validate the automatic BA analysis. First BA analysis was executed on 23 manually annotated CT scans (11 CF, 12 control subjects) to compare automatic with manual BA-analysis outcomes. Furthermore, the BA analysis was executed on two longitudinal datasets (Copenhagen 111 CTs, ataluren 347 CTs) to assess longitudinal BA changes and compare them with manual scoring results.

RESULTS:

The automatic and manual BA analysis showed no significant differences in quantifying bronchi. For the longitudinal datasets the automatic BA analysis detected 247 and 347 BA pairs/CT in the Copenhagen and ataluren dataset, respectively. A significant increase of 0.02 of Bout/A and Bin/A was detected for Copenhagen dataset over an interval of 2 years, and 0.03 of Bout/A and 0.02 of Bin/A for ataluren dataset over an interval of 48 weeks (all p<0.001). The progression of 0.01 of Bwt/A was detected only in the ataluren dataset (p<0.001). BA-analysis outcomes showed weak to strong correlations (correlation coefficient from 0.29 to 0.84) with manual scoring results for airway disease.

CONCLUSION:

The BA analysis can fully automatically analyse a large number of BA pairs on chest CTs to detect and monitor progression of bronchial wall thickening and bronchial widening in patients with CF.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Transtornos Respiratórios / Fibrose Cística Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Holanda

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Transtornos Respiratórios / Fibrose Cística Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Holanda