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

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 t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jihye Yun, Young Hoon Cho, Sang Min Lee, Jeongeun Hwang, Jae Seung Lee, Yeon-Mok Oh, Sang-Do Lee, Li-Cher Loh, Choo-Khoon Ong, Joon Beom Seo, Namkug Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/88b1bb1db855437aabb5bc493f7c6609
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:88b1bb1db855437aabb5bc493f7c6609
record_format	dspace
spelling	oai:doaj.org-article:88b1bb1db855437aabb5bc493f7c66092021-12-02T16:06:42ZDeep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease10.1038/s41598-021-94535-42045-2322https://doaj.org/article/88b1bb1db855437aabb5bc493f7c66092021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94535-4https://doaj.org/toc/2045-2322Abstract 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.Jihye YunYoung Hoon ChoSang Min LeeJeongeun HwangJae Seung LeeYeon-Mok OhSang-Do LeeLi-Cher LohChoo-Khoon OngJoon Beom SeoNamkug KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jihye Yun Young Hoon Cho Sang Min Lee Jeongeun Hwang Jae Seung Lee Yeon-Mok Oh Sang-Do Lee Li-Cher Loh Choo-Khoon Ong Joon Beom Seo Namkug Kim Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease
description	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.
format	article
author	Jihye Yun Young Hoon Cho Sang Min Lee Jeongeun Hwang Jae Seung Lee Yeon-Mok Oh Sang-Do Lee Li-Cher Loh Choo-Khoon Ong Joon Beom Seo Namkug Kim
author_facet	Jihye Yun Young Hoon Cho Sang Min Lee Jeongeun Hwang Jae Seung Lee Yeon-Mok Oh Sang-Do Lee Li-Cher Loh Choo-Khoon Ong Joon Beom Seo Namkug Kim
author_sort	Jihye Yun
title	Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease
title_short	Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease
title_full	Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease
title_fullStr	Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease
title_full_unstemmed	Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease
title_sort	deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/88b1bb1db855437aabb5bc493f7c6609
work_keys_str_mv	AT jihyeyun deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease AT younghooncho deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease AT sangminlee deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease AT jeongeunhwang deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease AT jaeseunglee deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease AT yeonmokoh deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease AT sangdolee deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease AT licherloh deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease AT chookhoonong deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease AT joonbeomseo deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease AT namkugkim deepradiomicsbasedsurvivalpredictioninpatientswithchronicobstructivepulmonarydisease
_version_	1718384912564748288

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

Ejemplares similares