Pediatric pulmonology · 2024
Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.
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De Filippo Maria, Fasola Salvatore, De Matteis Federica, Gorone Maria Sole Prevedoni, Preda Lorenzo, Votto Martina, Malizia Velia, Marseglia Gian Luigi, La Grutta Stefania, Licari Amelia
Аннотация
OBJECTIVES: Chest high-resolution computed tomography (HRCT) is conditionally recommended to rule out conditions that mimic or coexist with severe asthma in children. However, it may provide valuable insights into identifying structural airway changes in pediatric patients. This study aims to develop a machine learning-based chest HRCT image analysis model to aid pediatric pulmonologists in identifying features of severe asthma.
METHODS: This retrospective case-control study compared children with severe asthma (as defined by ERS/ATS guidelines) to age- and sex-matched controls without asthma, using chest HRCT scans for detailed imaging analysis. Statistical analysis included classification trees, random forests, and conventional ROC analysis to identify the most significant imaging features that mark severe asthma from controls.
RESULTS: Chest HRCT scans differentiated children with severe asthma from controls. Compared to controls (n = 21, mean age 11.4 years), children with severe asthma (n = 20, mean age 10.4 years) showed significantly greater bronchial thickening (BT) scores (p < 0.001), airway wall thickness percentage (AWT%, p < 0.001), bronchiectasis grading (BG) and bronchiectasis severity (BS) scores (p = 0.016), mucus plugging, and centrilobular emphysema (p = 0.009). Using AWT% as the predictor in conventional ROC analysis, an AWT% ≥ 38.6 emerged as the optimal classifier for discriminating severe asthmatics from controls, with 95% sensitivity, specificity, and overall accuracy.
CONCLUSION: Our study demonstrates the potential of machine learning-based analysis of chest HRCT scans to accurately identify features associated with severe asthma in children, enhancing diagnostic evaluation and contributing to the development of more targeted treatment approaches.
METHODS: This retrospective case-control study compared children with severe asthma (as defined by ERS/ATS guidelines) to age- and sex-matched controls without asthma, using chest HRCT scans for detailed imaging analysis. Statistical analysis included classification trees, random forests, and conventional ROC analysis to identify the most significant imaging features that mark severe asthma from controls.
RESULTS: Chest HRCT scans differentiated children with severe asthma from controls. Compared to controls (n = 21, mean age 11.4 years), children with severe asthma (n = 20, mean age 10.4 years) showed significantly greater bronchial thickening (BT) scores (p < 0.001), airway wall thickness percentage (AWT%, p < 0.001), bronchiectasis grading (BG) and bronchiectasis severity (BS) scores (p = 0.016), mucus plugging, and centrilobular emphysema (p = 0.009). Using AWT% as the predictor in conventional ROC analysis, an AWT% ≥ 38.6 emerged as the optimal classifier for discriminating severe asthmatics from controls, with 95% sensitivity, specificity, and overall accuracy.
CONCLUSION: Our study demonstrates the potential of machine learning-based analysis of chest HRCT scans to accurately identify features associated with severe asthma in children, enhancing diagnostic evaluation and contributing to the development of more targeted treatment approaches.