ArticleScientific reports2021
Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Chest CT in the evaluation of chronic obstructive pulmonary disease: recommendations of Asian Society of Thoracic Radiology.The British journal of radiology · 2026Guideline
- Seeing is believing-on the utility of CT in phenotyping chronic obstructive pulmonary disease.The British journal of radiology · 2026Review
- Radiomic analysis of computed tomography scans in COPD patients relates to different clinical and biological features, and identifies different lung function trajectories during follow-up.ERJ open research · 2026Article
- Bronchiectasis in patients with chronic obstructive pulmonary disease: AI-based CT quantification using the bronchial tapering ratio.European radiology · 2026Article
- Prognostic Models for Disease Progression and Outcomes in Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Global trends and hotspots in AI applications for CT detection of chronic obstructive pulmonary disease: A bibliometric analysis from 2012 to 2024.Lasers in medical science · 2025Review
- CT-Based Radiomics Enhance Respiratory Function Analysis for Lung SBRT.Bioengineering (Basel, Switzerland) · 2025Article
- Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.Respiratory research · 2024Article
- Development and validation of a nomogram model for mortality prediction in stable chronic obstructive pulmonary disease patients: A prospective observational study in the RealDTC cohort.Journal of global health · 2024Observational
- CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD.Military Medical Research · 2024Article
- Radiomics-Assisted Computed Tomography-Based Analysis to Evaluate Lung Morphology Characteristics after Congenital Diaphragmatic Hernia.Journal of clinical medicine · 2023Article
- Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model.PLoS computational biology · 2023Article
- Respiratory microbiota and radiomics features in the stable COPD patients.Respiratory research · 2023Article
- Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification.Journal of healthcare engineering · 2023Article
- Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD.Frontiers in medicine · 2022Article
- Early COPD Risk Decision for Adults Aged From 40 to 79 Years Based on Lung Radiomics Features.Frontiers in medicine · 2022Article
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Authors and funding
11 authors.
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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.
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