Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma

doi:10.1007/s00259-021-05573-z

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

	Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma
	An, Chao 5,7; Li, Dongyang 4,5; Li, Sheng 1; Li, Wangzhong 3; Tong, Tong 4,6; Liu, Lizhi 1; Jiang, Dongping 1; Jiang, Linling 1; Ruan, Guangying 1; Hai, Ning 2
刊名	EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
	2021-10-15
页码	13
关键词	Lymph node metastases Pancreatic ductal adenocarcinoma Deep learning Dual-energy computed tomography Prognosis
ISSN号	1619-7070
DOI	10.1007/s00259-021-05573-z
通讯作者	Wang, Kun(kun.wang@ia.ac.cn) ; Zhuo, Shuiqing(zhuosq@sysucc.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Purpose Diagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learning radiomics (DLR) models of dual-energy computed tomography (DECT) to classify LNM status of PDAC and to stratify the overall survival before treatment. Methods From August 2016 to October 2020, 148 PDAC patients underwent regional lymph node dissection and scanned preoperatively DECT were enrolled. The virtual monoenergetic image at 40 keV was reconstructed from 100 and 150 keV of DECT. By setting January 1, 2021, as the cut-off date, 113 patients were assigned into the primary set, and 35 were in the test set. DLR models using VMI 40 keV, 100 keV, 150 keV, and 100 + 150 keV images were developed and compared. The best model was integrated with key clinical features selected by multivariate Cox regression analysis to achieve the most accurate prediction. Results DLR based on 100 + 150 keV DECT yields the best performance in predicting LNM status with the AUC of 0.87 (95% confidence interval [CI]: 0.85-0.89) in the test cohort. After integrating key clinical features (CT-reported T stage, LN status, glutamyl transpeptadase, and glucose), the AUC was improved to 0.92 (95% CI: 0.91-0.94). Patients at high risk of LNM portended significantly worse overall survival than those at low risk after surgery (P = 0.012). Conclusions The DLR model showed outstanding performance for predicting LNM in PADC and hold promise of improving clinical decision-making.
资助项目	Ministry of Science and Technology of China[2017YFA0205200] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81930053] ; Youth Innovation Promotion Association CAS[Y202040] ; Project of High-Level Talents Team Introduction in Zhuhai City[HLHPTP201703]
WOS关键词	CHEMORADIATION THERAPY ; CANCER ; CT ; INVOLVEMENT ; CA19-9 ; MARKER ; RISK
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000707292600013
资助机构	Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS ; Project of High-Level Talents Team Introduction in Zhuhai City
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46221]
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	Wang, Kun; Zhuo, Shuiqing; Tian, Jie
作者单位	1.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, Dept Radiol, Canc Ctr,State Key Lab Oncol South China, Guangzhou 510060, Peoples R China 2.Capital Med Univ, Beijing Chao Yang Hosp, Dept Ultrasound, Beijing 100010, Peoples R China 3.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, Dept Nasopharyngeal Carcinoma, Canc Ctr,State Key Lab Oncol South China, Guangzhou 510060, Peoples R China 4.Chinese Acad Sci, Beijing Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Inst Automat,CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 5.Beihang Univ, Sch Engn Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 7.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, Dept Minimal Invas Intervent, Canc Ctr,State Key Lab Oncol South China, Guangzhou 510060, Peoples R China 8.Chinese Acad Med Sci & Peking Union Med Coll, Canc Hosp, Natl Clin Res Ctr Canc, Natl Canc Ctr,Dept Intervent Therapy, 17 Panjiayuan Nanli, Beijing 100021, Peoples R China
推荐引用方式 GB/T 7714	An, Chao,Li, Dongyang,Li, Sheng,et al. Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma[J]. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING,2021:13.
APA	An, Chao.,Li, Dongyang.,Li, Sheng.,Li, Wangzhong.,Tong, Tong.,...&Tian, Jie.(2021).Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma.EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING,13.
MLA	An, Chao,et al."Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma".EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021):13.