Rationale: Conventionally considered irreversible, bronchiectasis has been demonstrated to be reversible in children in small studies. However, the factors associated with radiographic reversibility of bronchiectasis have yet to be defined. Objectives: In a large cohort of children with bronchiectasis, we aimed to determine: 1) if and to what extent bronchiectasis is reversible and 2) factors associated with radiographic chest high-resolution computed tomography (cHRCT) resolution. Methods: We identified children with bronchiectasis who had a repeat multidetector cHRCT scan between 2010 and 2021. We excluded those with cystic fibrosis, surgical pulmonary resection, traction bronchiectasis only, or lobar opacification. Measurements and Main Results: cHRCT scans were scored using the modified Reiff score (MRS) with a pediatric correction. Resolution was defined as an absence of abnormal bronchoarterial ratio (>0.8) on the second cHRCT scan. We included 142 children (median age, 5 years; IQR, 2.6–7.4). Inter- and intrarater agreement in MRSs was excellent (weighted κ = 0.83–0.86 and 0.95, respectively). There was radiographic resolution in 57 of 142 patients (40.1%), improvement in 56 of 142 (39.4%), and no change or worsening in 29 of 142 (20.4%). Pseudomonas aeruginosa (PsA) was absolutely associated with a lack of resolution. On multivariable regression, in those without PsA cultured, younger age at the time of diagnosis (risk ratio [RR], 0.94; 95% confidence interval [CI], 0.88–0.99), lower MRS (RR, 0.89; 95% CI, 0.82–0.97), and lower annual rate of exacerbations requiring intravenous antibiotic therapy (RR, 0.60; 95% CI, 0.37–0.98) increased the likelihood of radiographic resolution. Conclusions: This first large cohort confirms that bronchiectasis in children is often reversible with appropriate management. Younger children and those with lesser radiographic severity at diagnosis were most likely to exhibit radiographic reversibility, whereas those with PsA infection were least likely.

Background: Interventional pediatric flexible bronchoscopy has many advantages over radiologic investigations in diagnosing uncommon congenital H-type tracheoesophageal fistula (TEF), dual TEF, bronchoesophageal fistula (BEF) and fistula recurrence including higher rates of identification and anatomic localization with guide wire cannulation. We compare the diagnostic utility of flexible bronchoscopy to radiologic techniques for congenital aerodigestive fistula. Methods: A single center retrospective review was completed of all cases of pediatric TEF and BEF diagnosed with flexible bronchoscopy between January 2000 and November 2020. Results: Fistulae were diagnosed 21 times in 18 patients at a median age of 1.22 years (interquartile range: 0.50 to 2.99). The median time from diagnosis to repair was 17.5 days (interquartile range: 5.5 to 43). Symptoms commonly related to fistula were found in all patients. Uncommon fistulae included single H-type TEF (n=10, 47.6%), dual H-type TEF (n=2, 9.5%), dual proximal and distal TEF with esophageal atresia (n=5, 23.8%), TEF recurrence (n=2, 14.3%), BEF (n=1, 4.8%), and a BEF recurrence (n=1, 4.8%). Flexible bronchoscopy confirmed the diagnosis in all fistulae using a guide wire cannulation or methylene blue dye injection. A combined procedure with simultaneous bronchoscopy and esophagoscopy was used for 6 fistulae. The positive examination rate was 75% for bronchoscopy compared with 2.6% for contrast swallow studies and 28.6% for tube esophagograms. Conclusions: Flexible bronchoscopy should be considered as a first line investigation in uncommon aerodigestive fistulae. In the absence of a skilled bronchoscopist, the best radiologic investigation is a pull-back tube esophagogram but may still require endoscopic confirmation at the time of fistula repair.

Bronchiectasis, one of the most neglected chronic lung conditions, has a high individual disease burden and economic cost and causes poor quality of life in children/adolescents and adults. Advances in image quality and a dramatic reduction in acquisition times, multiple high-resolution chest tomography (HRCT) acquisitions, and reconstructions of the lung have resulted in accurate categorization and determination of the extent of lung parenchyma and airway abnormality. For bronchiectasis, the diagnosis is confirmed using the key feature of abnormally increased broncho-arterial (B.A.) ratio (BAR), with or without other abnormalities, e.g., bronchial wall thickening, lack of bronchial tapering, and mucus plugging. Most of these features require shape analysis of the airway and artery regions to perform various assessments that can have inter-rater variability and are time-consuming. This challenge is amplified in pediatric patients due to age-related anatomical variations. The anatomical differences and variations in airway structures between Infants, Early Childhood, and middle Childhood can impact how the images can be processed and analyzed. To address this, we proposed two novel image-processing methods to detect and measure the B.A. pairs. The first method uses an optimized connected component labelling (CCL) algorithm to construct bounding boxes around the objects (airway, artery) and extract the regions of interest (ROIs) for potential B.A. pairs. The second method allows us to calculate 4 or 6 diameters for each object in the ROIs and use their mean value as the final diameter, demonstrating agreement with manual readings. Evaluating against a diverse set of HRCT scans from various categories validates our proposed methods’ significance and practical utility in detecting and measuring the disjointed B.A. pairs to assess increased BAR.

Bronchiectasis in children can progress to a severe lung condition if not diagnosed and treated early. The radiological diagnostic criteria for the diagnosis of bronchiectasis is an increased broncho-arterial (BA) ratio. From high-resolution computed tomography (HRCT) scans, the BA pairs must be detected first to derive the BA ratio. This study aims to identify potential BA pairs from HRCT scans of children undertaken to evaluate suppurative lung disease through an automated approach. After segmenting the lung regions, the HRCT scans are cleaned using a histogram analysis-based approach followed by a potential arteries identification process comprising four conditions based on imaging features. Potential arteries and their connected components are extracted, and potential bronchi are identified. Finally, the coordinates of potential arteries and potential bronchi are matched as the last step of BA pairs extraction. A total of 8–50 BA pairs are detected for each patient. Additionally, the area and several diameters of the bronchi and arteries are measured, and BA ratios based on these are calculated. Through this approach, the BA pairs of a CT scan datasets are detected and utilizing a deep learning model, a high classification test accuracy of 98.53% is achieved, validating the robustness of the proposed BA detection approach. The results show that visible BA pairs can be identified and segmented automatically, and the BA ratio calculated may help diagnose bronchiectasis with less effort and time.