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

Ju Gang Nam, Hye Rin Kang, Sang Min Lee, Hyungjin Kim, Chanyoung Rhee, Jin Mo Goo, Yeon Mok Oh, Chang Hoon Lee, Chang Min Park

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Pages (from-to)199-208
Number of pages10
JournalRadiology
Volume305
Issue number1
DOIs
StatePublished - Oct 2022

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