Mucus plugging, air trapping, and bronchiectasis are important outcome measures in assessing progressive childhood cystic fibrosis lung disease

Robinson, Terry E.; Goris, Michael L.; Moss, Richard B.; Tian, Lu; Kan, Peiyi; Yilma, Mignote; McCoy, Karen; Newman, Beverley; Jong, Pim A. de; Long, Frederick R.; Brody, Alan S.; Behrje, Rhett; Yates, Denise; Cornfield, David N.

doi:10.1002/ppul.24646

Cited by 17 publications

(17 citation statements)

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“…These findings were in concordance with radiologist visual assessment and with mucus plugging scores. With the ability of our DN model to detect unique features, our model was less prone to errors associated with insufficient exhalation during expiratory CT acquisitions than a threshold-based technique, and therefore may not require a correction factor for differing deflation maneuvers over the course of four different testing periods as was needed by Robinson and colleagues [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

“…CF subjects were school-age children accrued as part of the Novartis/CF Foundation Therapeutics multicenter prospective 2-year natural history study [ 26 ]. CT and clinical data were acquired from two different sites, referred to as Site 1 (N = 24) and Site 2 (N = 12) ( Table 1 ), with baseline and follow-up examinations at 3, 12, and 24 months.…”

Section: Methodsmentioning

confidence: 99%

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Improved detection of air trapping on expiratory computed tomography using deep learning

et al. 2021

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Background Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. Objective To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. Materials and methods Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. Results QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. Conclusion The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Improved detection of air trapping on expiratory computed tomography using deep learning

et al. 2021

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Section: Discussionmentioning

confidence: 99%

“…CF subjects were school-age children accrued as part of the Novartis/CF Foundation Therapeutics multicenter prospective 2-year natural history study [24]. CT and clinical data were acquired from two different sites, referred to as Site 1 (N=24) and Site 2 (N=12) ( Table 1 ), with baseline and follow-up examinations at 3, 12, and 24 months.…”

Section: Methodsmentioning

confidence: 99%

Improved Detection of Air Trapping on Expiratory Computed Tomography Using Deep Learning

Ram

Hoff

Bell

et al. 2020

Preprint

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BackgroundRadiologic evidence of air trapping (AT) on expiratory CT scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression.ObjectiveTo investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques.Materials and MethodsPaired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model.ResultsQAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach.ConclusionThe CNN model effectively delineated air trapping on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring air trapping in CF patients.

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“…A recent study of gender differences was able to detect statistically different Bhalla scores in age‐matched females with CF compared with males, correlating with worse BMI and lung function 38 . Additionally, other investigators have shown that findings on CT (particularly mucus plugging and air trapping) are able to predict worsening CF lung disease over time 39 as well as respond to clinically proven modulator therapies 24…”

Section: Computed Tomographymentioning

confidence: 98%

Novel imaging techniques for cystic fibrosis lung disease

Goralski

Stewart

Woods

2021

Pediatric Pulmonology

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With an increasing number of patients with cystic fibrosis (CF) receiving highly effective CFTR (cystic fibrosis transmembrane regulator protein) modulator therapy, particularly at a young age, there is an increasing need to identify imaging tools that can detect and regionally visualize mild CF lung disease and subtle changes in disease state. In this review, we discuss the latest developments in imaging modalities for both structural and functional imaging of the lung available to CF clinicians and researchers, from the widely available, clinically utilized imaging methods for assessing CF lung disease—chest radiography and computed tomography—to newer techniques poised to become the next phase of clinical tools—structural/functional proton and hyperpolarized gas magnetic resonance imaging (MRI). Finally, we provide a brief discussion of several newer lung imaging techniques that are currently available only in selected research settings, including chest tomosynthesis, and fluorinated gas MRI. We provide an update on the clinical and/or research status of each technique, with a focus on sensitivity, early disease detection, and possibilities for monitoring treatment efficacy.

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Mucus plugging, air trapping, and bronchiectasis are important outcome measures in assessing progressive childhood cystic fibrosis lung disease

Cited by 17 publications

References 25 publications

Improved detection of air trapping on expiratory computed tomography using deep learning

Improved detection of air trapping on expiratory computed tomography using deep learning

Improved Detection of Air Trapping on Expiratory Computed Tomography Using Deep Learning

Novel imaging techniques for cystic fibrosis lung disease

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