Deep Learning-Based Prediction Model Using Radiography in Nontuberculous Mycobacterial Pulmonary Disease

Abstract

BACKGROUND: Prognostic prediction of nontuberculous mycobacteria pulmonary disease using a deep learning technique has not been attempted. RESEARCH QUESTION: Can a deep learning (DL) model using chest radiography predict the prognosis of nontuberculous mycobacteria pulmonary disease? STUDY DESIGN AND METHODS: Patients who received a diagnosis of nontuberculous mycobacteria pulmonary disease at Seoul National University Hospital (training and validation dataset) between January 2000 and December 2015 and at Seoul Metropolitan GovernmentBoramae Medical Center (test dataset) between January 2006 and December 2015 were included. We trained DL models to predict the 3-, 5-, and 10-year overall mortality using baseline chest radiographs at diagnosis. We tested the predictability for the corresponding mortality using only DL-driven radiographic scores and using both radiographic scores and clinical information (age, sex, BMI, and mycobacterial species). RESULTS: The datasets comprised 1,638 (training and validation set) and 566 (test set) chest radiographs from 1,034 and 200 patients, respectively. The Dl-driven radiographic score provided areas under the receiver operating characteristic curve (AUC) of 0.844, 0.781, and 0.792 for 10-, 5-, and 3-year mortality, respectively. The logistic regression model using both the radiographic score and clinical information provided AUCs of 0.922, 0.942, and 0.865 for the 10-, 5, and 3-year mortality, respectively. INTERPRETATION: The DL model we developed could predict the mid-term to-long-term mortality of patients with nontuberculous mycobacteria pulmonary disease using a baseline radiograph at diagnosis, and the predictability increased with clinical information.