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
发表期刊EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
ISSN1619-7070
2021-10-15
页码13
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.

关键词Lymph node metastases Pancreatic ductal adenocarcinoma Deep learning Dual-energy computed tomography Prognosis
DOI10.1007/s00259-021-05573-z
关键词[WOS]CHEMORADIATION THERAPY ; CANCER ; CT ; INVOLVEMENT ; CA19-9 ; MARKER ; RISK
收录类别SCI
语种英语
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]
项目资助者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
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
WOS类目Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:000707292600013
出版者SPRINGER
引用统计
被引频次:26[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/46222
专题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 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
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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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