Deep Learning Prediction of Survival in Patients with Chronic Obstructive Pulmonary Disease Using Chest Radiographs.

Background Preexisting indexes for predicting the prognosis of chronic obstructive pulmonary disease (COPD) do not use radiologic information and are impractical because they involve complex history assessments or exercise tests. Purpose To develop and to validate a deep learning-based survival prediction model in patients with COPD (DLSP) using chest radiographs, in addition to other clinical factors. Materials and Methods In this retrospective study, data from patients with COPD who underwent postbronchodilator spirometry and chest radiography from 2011-2015 were collected and split into training ( n = 3475), validation ( n = 435), and internal test ( n = 315) data sets. The algorithm for predicting survival from chest radiographs was trained (hereafter, DLSP CXR ), and then age, body mass index, and forced expiratory volume in 1 second (FEV 1 ) were integrated within the model (hereafter, DLSP integ ). For external test, three independent cohorts were collected ( n = 394, 416, and 337). The discrimination performance of DLSP CXR was evaluated by using time-dependent area under the receiver operating characteristic curves (TD AUCs) at 5-year survival. Goodness of fit was assessed by using the Hosmer-Lemeshow test. Using one external test data set, DLSP integ was compared with four COPD-specific clinical indexes: BODE, ADO, COPD Assessment Test (CAT), and St George's Respiratory Questionnaire (SGRQ). Results DLSP CXR had a higher performance at predicting 5-year survival than FEV 1 in two of the three external test cohorts (TD AUC: 0.73 vs 0.63 [ P = .004]; 0.67 vs 0.60 [ P = .01]; 0.76 vs 0.77 [ P = .91]). DLSP CXR demonstrated good calibration in all cohorts. The DLSP integ model showed no differences in TD AUC compared with BODE (0.87 vs 0.80; P = .34), ADO (0.86 vs 0.89; P = .51), and SGRQ (0.86 vs 0.70; P = .09), and showed higher TD AUC than CAT (0.93 vs 0.55; P < .001). Conclusion A deep learning model using chest radiographs was capable of predicting survival in patients with chronic obstructive pulmonary disease. © RSNA, 2022 Online supplemental material is available for this article.