A Deep Learning Based Radiomic Classifier for Usual Interstitial Pneumonia.
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Abstract
As chest computed tomography (CT) has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT interpretation are urgently needed.Does a deep learning based (DL) classifier for usual interstitial pneumonia (UIP) derived using CT features accurately discriminate radiologist-determined visual UIP?A retrospective cohort study was performed. Chest CT 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 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 multi-center ILD clinical cohort. Transplant-free survival was compared between UIP classification approaches using the Kaplan-Meier estimator and Cox proportional hazards regression.2,907 chest CTs were included in the training (n=1934), validation (n=408), performance (n=565) datasets. The prevalence of radiologist determined visual UIP was 12.4% and 37.1% in the performance and ILD clinical cohorts, respectively. The DL UIP classifier predicted visual UIP in the performance cohort with sensitivity and specificity of 93% and 86%, respectively, and in the multi-center ILD clinical cohort with 81% and 77%, respectively. DL and visual UIP classification similarly discriminated survival, and outcomes were consistent among cases with positive DL UIP classification irrespective of visual classification.A DL classifier for UIP demonstrated good test performance across a wide range of UIP prevalence and similarly discriminated survival when compared to radiologist determined UIP. This automated tool could efficiently screen for UIP in patients undergoing chest CT and identify a high-risk phenotype among those with known ILD.Copyright © 2023. Published by Elsevier Inc.