Developing and Validation of a Multimodal-based Machine Learning Model for Diagnosis of Usual Interstitial Pneumonia: a Prospective Multicenter Study.
Authors
Affiliations (10)
Affiliations (10)
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China.
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Department of Radiology, China-Japan Friendship Hospital, Beijing, China.
- Capital Medical University, Beijing, China; Department of Radiology, China-Japan Friendship Hospital, Beijing, China.
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China; Capital Medical University, Beijing, China.
- National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China.
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Department of Radiology, The Second Hospital of Jilin University, Changchun, 130041, China.
- Academy for Multidisciplinary Studies, Capital Normal University, Beijing,100048, China.
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Capital Medical University, Beijing, China; Department of Radiology, China-Japan Friendship Hospital, Beijing, China. Electronic address: [email protected].
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China; Capital Medical University, Beijing, China. Electronic address: [email protected].
Abstract
Usual Interstitial Pneumonia (UIP) indicates poor prognosis, and there is significant heterogeneity in the diagnosis of UIP, necessitating an auxiliary diagnostic tool. Can a machine learning (ML) classifier using radiomics features and clinical data accurately identify UIP from patients with interstitial lung diseases (ILD)? This dataset from a prospective cohort consists of 5321 sets of high-resolution computed tomography (HRCT) images from 2901 patients with ILD (male: 63.5%, age: 61.7 ± 10.8 years) across three medical centers. Multimodal data, including whole-lung radiomics features on HRCT and demographics, smoking, lung function, and comorbidity data, were extracted. An eXtreme Gradient Boosting (XGBoost) and logistic regression were used to design a nomogram predicting UIP or not. Area under the receiver operating characteristic curve (AUC) and Cox's regression for all-cause mortality were used to assess the diagnostic performance and prognostic value of models, respectively. 5213 HRCT image datasets were divided into the training group (n=3639), the internal testing group (n=785), and the external validation group (n=789). UIP prevalence was 43.7% across the whole dataset, with 42.7% and 41.3% for the internal validation set and external validation set. The radiomics-based classifier had an AUC of 0.790 in the internal testing set and 0.786 for the external validation dataset. Integrating multimodal data improved AUCs to 0.802 and 0.794, respectively. The performance of the integration model was comparable to pulmonologist with over 10 years of experience in ILD. Within 522 patients deceased during a median follow-up period of 3.37 years, the multimodal-based ML model-predicted UIP status was associated with high all-cause mortality risk (hazard ratio: 2.52, p<0.001). The classifier combining radiomics and clinical features showed strong performance across varied UIP prevalence. This multimodal-based ML model could serve as an adjunct in the diagnosis of UIP.