CASIA OpenIR  > 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, Chao1,2; Li, Dongyang2,3; Li, Sheng4; Li, Wangzhong5; Tong, Tong3,6; Liu, Lizhi4; Jiang, Dongping4; Jiang, Linling4; Ruan, Guangying4; Hai, Ning7; Fu, Yan8; Wang, Kun3,6; Zhuo, Shuiqing4; Tian, Jie2,3,6
Source PublicationEUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
ISSN1619-7070
2021-10-15
Pages13
Abstract

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.

KeywordLymph node metastases Pancreatic ductal adenocarcinoma Deep learning Dual-energy computed tomography Prognosis
DOI10.1007/s00259-021-05573-z
WOS KeywordCHEMORADIATION THERAPY ; CANCER ; CT ; INVOLVEMENT ; CA19-9 ; MARKER ; RISK
Indexed BySCI
Language英语
Funding ProjectMinistry 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]
Funding OrganizationMinistry 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
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000707292600013
PublisherSPRINGER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46222
Collection中国科学院分子影像重点实验室
Corresponding AuthorWang, Kun; Zhuo, Shuiqing; Tian, Jie
Affiliation1.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
2.Beihang Univ, Sch Engn Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
3.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
4.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, Dept Radiol, Canc Ctr,State Key Lab Oncol South China, Guangzhou 510060, Peoples R China
5.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
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
7.Capital Med Univ, Beijing Chao Yang Hosp, Dept Ultrasound, Beijing 100010, 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
Recommended Citation
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.
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