Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging

doi:10.1111/1759-7714.14305

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging
	Qiu, Zhixin 1; Wu, Qingxia 6; Wang, Shuo 2,7; Chen, Zhixia 3; Lin, Feng 4; Zhou, Yuyan 1; Jin, Jing 1; Xian, Jinghong 5; Tian, Jie 2,6,7; Li, Weimin 1
刊名	THORACIC CANCER
	2022-01-06
页码	11
关键词	deep learning ground-glass nodules multiple timepoints sequential
ISSN号	1759-7706
DOI	10.1111/1759-7714.14305
通讯作者	Tian, Jie(jie.tian@ia.ac.cn) ; Li, Weimin(weimi003@scu.edu.cn)
英文摘要	Background Early identification of the malignant propensity of pulmonary ground-glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning-based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs. Methods This diagnostic study retrospectively enrolled 762 patients with GGNs from West China Hospital of Sichuan University between July 2009 and March 2019. All patients underwent surgical resection and at least two consecutive time-point CT scans. We developed a deep learning-based method to identify GGNs using sequential CT imaging on a training set consisting of 1524 CT sections from 508 patients and then evaluated 256 patients in the testing set. Afterwards, an observer study was conducted to compare the diagnostic performance between the deep learning model and two trained radiologists in the testing set. We further performed stratified analysis to further relieve the impact of histological types, nodule size, time interval between two CTs, and the component of GGNs. Receiver operating characteristic (ROC) analysis was used to assess the performance of all models. Results The deep learning model that used integrated DL-features from initial and follow-up CT images yielded the best diagnostic performance, with an area under the curve of 0.841. The observer study showed that the accuracies for the deep learning model, junior radiologist, and senior radiologist were 77.17%, 66.89%, and 77.03%, respectively. Stratified analyses showed that the deep learning model and radiologists exhibited higher performance in the subgroup of nodule sizes larger than 10 mm. With a longer time interval between two CTs, the deep learning model yielded higher diagnostic accuracy, but no general rules were yielded for radiologists. Different densities of components did not affect the performance of the deep learning model. In contrast, the radiologists were affected by the nodule component. Conclusions Deep learning can achieve diagnostic performance on par with or better than radiologists in identifying pulmonary GGNs.
资助项目	Central Government Guided Local Science and Technology Free Exploration Projects of Sichuan Province, China[2020ZYD005] ; China Postdoctoral Science Foundation[2019TQ0019] ; National Key R&D Program of China[2017YFA0205200] ; National Natural Science Foundation of China[81700095] ; National Natural Science Foundation of China[91859203] ; National Natural Science Foundation of China[81871890] ; National Natural Science Foundation of China[81930053] ; Science and Technology Plan Project of Sichuan Province, China[2019YFS0335]
WOS关键词	LUNG-CANCER ; NATURAL-HISTORY ; FOLLOW-UP ; CT ; OPACITY ; LONG ; ADENOCARCINOMAS ; GUIDELINES ; FEATURES ; LESIONS
WOS研究方向	Oncology ; Respiratory System
语种	英语
出版者	WILEY
WOS记录号	WOS:000739720300001
资助机构	Central Government Guided Local Science and Technology Free Exploration Projects of Sichuan Province, China ; China Postdoctoral Science Foundation ; National Key R&D Program of China ; National Natural Science Foundation of China ; Science and Technology Plan Project of Sichuan Province, China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47181]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Tian, Jie; Li, Weimin
作者单位	1.Sichuan Univ, Dept Resp & Crit Care Med, West China Hosp, Chengdu 610041, Peoples R China 2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China 3.Sichuan Univ, West China Hosp, Dept Radiol, Chengdu, Peoples R China 4.Sichuan Univ, West China Hosp, Dept Thorac Surg, Chengdu, Peoples R China 5.Sichuan Univ, West China Hosp, Dept Clin Res, Chengdu, Peoples R China 6.Northeastern Univ, Coll Med & Biomed Informat Engn, Shenyang, Peoples R China 7.Chinese Acad Sci, CAS Key Lab Mol Imaging, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Qiu, Zhixin,Wu, Qingxia,Wang, Shuo,et al. Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging[J]. THORACIC CANCER,2022:11.
APA	Qiu, Zhixin.,Wu, Qingxia.,Wang, Shuo.,Chen, Zhixia.,Lin, Feng.,...&Li, Weimin.(2022).Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging.THORACIC CANCER,11.
MLA	Qiu, Zhixin,et al."Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging".THORACIC CANCER (2022):11.