Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study | |
Dong, Di2,3; Fang, Mengjie2,3; Tang, Lei1; Shan, Xiuhong4; Gao, Jianbo5; Giganti, Francesco6,7,8; Wang, Rongpin9; Chen, Xin10,11; Wang, Xiaoxiao4; Palumbo, Diego8,12 | |
刊名 | ANNALS OF ONCOLOGY |
2020-07-01 | |
卷号 | 31期号:7页码:912-920 |
关键词 | deep learning locally advanced gastric cancer lymph node metastasis radiomic nomogram |
ISSN号 | 0923-7534 |
DOI | 10.1016/j.annonc.2020.04.003 |
英文摘要 | Background: Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough. Patients and methods: We enrolled 730 LAGC patients from five centers in China and one center in Italy, and divided them into one primary cohort, three external validation cohorts, and one international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multiphase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis. Results: The DLRN showed good discrimination of the number of LNM on all cohorts [overall C-indexes (95% confidence interval): 0.821 (0.785e0.858) in the primary cohort, 0.797 (0.771e0.823) in the external validation cohorts, and 0.822 (0.756e0.887) in the international validation cohort]. The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (P < 0.05). Besides, DLRN was significantly associated with the overall survival of LAGC patients (n ¼ 271). Conclusion: A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC. |
资助项目 | National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81771912] ; National Natural Science Foundation of China[81671682] ; National Natural Science Foundation of China[91959205] ; National Natural Science Foundation of China[81601469] ; National Natural Science Foundation of China[81701687] ; National Natural Science Foundation of China[81227901] ; National Science Fund for Distinguished Young Scholars[81925023] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFC1309101] ; National Key R&D Program of China[2017YFC1309104] ; Beijing Natural Science Foundation[L182061] ; Beijing Natural Science Foundation[Z180001] ; Youth Innovation Promotion Association CAS[2017175] ; UCL Graduate Research Scholarship ; Brahm PhD scholarship in memory of Chris Adams |
WOS关键词 | DIAGNOSIS ; SURVIVAL |
WOS研究方向 | Oncology |
语种 | 英语 |
出版者 | ELSEVIER |
WOS记录号 | WOS:000540695500014 |
资助机构 | National Natural Science Foundation of China ; National Science Fund for Distinguished Young Scholars ; National Key R&D Program of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS ; UCL Graduate Research Scholarship ; Brahm PhD scholarship in memory of Chris Adams |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/39812] |
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 | Ji, Jiafu; Liu, Zaiyi; Tian, Jie |
作者单位 | 1.Peking Univ Canc Hosp & Inst, Key Lab Carcinogenesis & Translat Res, Radiol Dept, Minist Educ Beijing, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Jiangsu Univ, Dept Radiol, Affiliated Peoples Hosp, Zhenjiang, Jiangsu, Peoples R China 5.Zhengzhou Univ, Dept Radiol, Affiliated Hosp 1, Zhengzhou, Henan, Peoples R China 6.Univ Coll London Hosp NHS Fdn Trust, Dept Radiol, London, England 7.UCL, Div Surg & Intervent Sci, Fac Med Sci, London, England 8.Ist Sci San Raffaele, Dept Radiol, Expt Imaging Ctr, Milan, Italy 9.Guizhou Prov Peoples Hosp, Dept Radiol, Guiyang, Guizhou, Peoples R China 10.Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Radiol, Guangzhou, Guangdong, Peoples R China |
推荐引用方式 GB/T 7714 | Dong, Di,Fang, Mengjie,Tang, Lei,et al. Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study[J]. ANNALS OF ONCOLOGY,2020,31(7):912-920. |
APA | Dong, Di.,Fang, Mengjie.,Tang, Lei.,Shan, Xiuhong.,Gao, Jianbo.,...&Tian, Jie.(2020).Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study.ANNALS OF ONCOLOGY,31(7),912-920. |
MLA | Dong, Di,et al."Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study".ANNALS OF ONCOLOGY 31.7(2020):912-920. |
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