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

Abstract

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, DLSPCXR), and then age, body mass index, and forced expiratory volume in 1 second (FEV1) were integrated within the model (hereafter, DLSPinteg). For external test, three independent cohorts were collected (n = 394, 416, and 337). The discrimination performance of DLSPCXR 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, DLSPinteg was compared with four COPD-specific clinical indexes: BODE, ADO, COPD Assessment Test (CAT), and St George's Respiratory Questionnaire (SGRQ). Results: DLSPCXR had a higher performance at predicting 5-year survival than FEV1 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]). DLSPCXR demonstrated good calibration in all cohorts. The DLSPinteg 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. (C) RSNA, 2022