Annals of the American Thoracic Society
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Rationale: The computed tomography (CT) pattern of definite or probable usual interstitial pneumonia (UIP) can be diagnostic of idiopathic pulmonary fibrosis and may obviate the need for invasive surgical biopsy. Few machine-learning studies have investigated the classification of interstitial lung disease (ILD) on CT imaging, but none have used histopathology as a reference standard. Objectives: To predict histopathologic UIP using deep learning of high-resolution computed tomography (HRCT). ⋯ The optimal cutoff point for classifying patients on the training cohort was 16.5% of virtual lung wedges with CNN-predicted UIP and resulted in sensitivity and specificity of 74% and 58%, respectively, in the testing cohort. CNN-predicted UIP was associated with an increased risk of death or lung transplantation during cross validation (hazard ratio, 1.5; 95% confidence interval, 1.1-2.2; P = 0.03). Conclusions: Virtual lung wedge resection in patients with ILD can be used as an input to a CNN for predicting the histopathologic UIP pattern and transplant-free survival.