A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia.

BACKGROUND: Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed. RESEARCH QUESTION: Does a deep learning (DL)-based classifier for usual interstitial pneumonia (UIP) derived using CT scan features accurately discriminate radiologist-determined visual UIP? STUDY DESIGN AND METHODS: A retrospective cohort study was performed. Chest CT scans acquired in individuals with and without ILD were drawn from a variety of public and private data sources. Using radiologist-determined visual UIP as ground truth, a convolutional neural network was used to learn discrete CT scan features of UIP, with outputs used to predict the likelihood of UIP using a linear support vector machine. Test performance characteristics were assessed in an independent performance cohort and multicenter ILD clinical cohort. Transplant-free survival was compared between UIP classification approaches using the Kaplan-Meier estimator and Cox proportional hazards regression. RESULTS: A total of 2,907 chest CT scans were included in the training (n = 1,934), validation (n = 408), and performance (n = 565) data sets. The prevalence of radiologist-determined visual UIP was 12.4% and 37.1% in the performance and ILD clinical cohorts, respectively. The DL-based UIP classifier predicted visual UIP in the performance cohort with sensitivity and specificity of 93% and 86%, respectively, and in the multicenter ILD clinical cohort with 81% and 77%, respectively. DL-based and visual UIP classification similarly discriminated survival, and outcomes were consistent among cases with positive DL-based UIP classification irrespective of visual classification. INTERPRETATION: A DL-based classifier for UIP demonstrated good test performance across a wide range of UIP prevalence and similarly discriminated survival when compared with radiologist-determined UIP. This automated tool could efficiently screen for UIP in patients undergoing chest CT scan and identify a high-risk phenotype among those with known ILD.

Occupational Lung Diseases: Spectrum of Common Imaging Manifestations.

Occupational lung diseases (OLD) are a group of preventable conditions caused by noxious inhalation exposure in the workplace. Workers in various industries are at a higher risk of developing OLD. Despite regulations contributing to a decreased incidence, OLD remain among the most frequently diagnosed work-related conditions, contributing to significant morbidity and mortality. A multidisciplinary discussion (MDD) is necessary for a timely diagnosis. Imaging, particularly computed tomography, plays a central role in diagnosing OLD and excluding other inhalational lung diseases. OLD can be broadly classified into fibrotic and non-fibrotic forms. Imaging reflects variable degrees of inflammation and fibrosis involving the airways, parenchyma, and pleura. Common manifestations include classical pneumoconioses, chronic granulomatous diseases (CGD), and small and large airway diseases. Imaging is influenced by the type of inciting exposure. The findings of airway disease may be subtle or solely uncovered upon expiration. High-resolution chest CT, including expiratory-phase imaging, should be performed in all patients with suspected OLD. Radiologists should familiarize themselves with these imaging features to improve diagnostic accuracy.