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Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease

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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.
Author(s)
윤지혜Young Hoon Cho이상민황정은이재승오연목이상도Li-Cher LohChoo-Khoon Ong서준범김남국
Issued Date
2021
Type
Article
Keyword
Biomedical engineeringChronic obstructive pulmonary diseaseComputed tomographyLung diseasesMortalityNeural networksObstructive lung diseasePatientsPrognosisRespiratory functionRisk assessmentSurvivalVertebrae
DOI
10.1038/s41598-021-94535-4
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8768
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_88b1bb1db855437aabb5bc493f7c6609&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Deep%20radiomics-based%20survival%20prediction%20in%20patients%20with%20chronic%20obstructive%20pulmonary%20disease&offset=0&pcAvailability=true
Publisher
SCIENTIFIC REPORTS
Location
영국
Language
영어
ISSN
2045-2322
Citation Volume
11
Citation Number
1
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Medicine > Medicine
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