1. Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease
- Author
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Namkug Kim, Choo-Khoon Ong, Yeon-Mok Oh, Joon Beom Seo, Jae Seung Lee, Li-Cher Loh, Sang Min Lee, Jeongeun Hwang, Young-Hoon Cho, Sang Do Lee, and Jihye Yun
- Subjects
Male ,medicine.medical_specialty ,Science ,Concordance ,Disease ,Article ,030218 nuclear medicine & medical imaging ,Pulmonary function testing ,Cohort Studies ,Pulmonary Disease, Chronic Obstructive ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,Radiomics ,Predictive Value of Tests ,Republic of Korea ,medicine ,Humans ,Aged ,COPD ,Multidisciplinary ,Lung ,business.industry ,Malaysia ,Middle Aged ,Prognosis ,medicine.disease ,Survival Analysis ,Obstructive lung disease ,Respiratory Function Tests ,medicine.anatomical_structure ,030228 respiratory system ,Cohort ,Medicine ,Female ,Neural Networks, Computer ,Radiology ,Tomography, X-Ray Computed ,business ,Biomedical engineering - Abstract
Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (> 440 m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642–0.8373) and 0.7156 (95% CI, 0.7024–0.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.
- Published
- 2021