CASIA OpenIR
Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer
Guo, Xu1,2; Liu, Zhenyu2,5; Sun, Caixia2,3,6; Zhang, Lei1; Wang, Ying7; Li, Ziyao1; Shi, Jiaxin1; Wu, Tong1; Cui, Hao1; Zhang, Jing8; Tian, Jie2,3,4,5,6; Tian, Jiawei1
Source PublicationEBIOMEDICINE
ISSN2352-3964
2020-10-01
Volume60Pages:11
Corresponding AuthorTian, Jie(jie.tian@ia.ac.cn) ; Tian, Jiawei(jwtian2004@163.com)
AbstractBackground: Completion axillary lymph node dissection is overtreatment for patients with sentinel lymph node (SLN) metastasis in whom the metastatic risk of residual non-SLN (NSLN) is low. However, the National Comprehensive Cancer Network panel posits that none of the previous studies has successfully identified such subset patients. Here, we develop a multicentre deep learning radiomics of ultrasonography model (DLRU) to predict the risk of SLN and NSLN metastasis. Methods: In total, 937 eligible breast cancer patients with ultrasound images were enrolled from two hospitals as the training set (n = 542) and independent test set (n = 395) respectively. Using the images, we developed and validated a prediction model combined with deep learning radiomics and axillary ultrasound to sequentially identify the metastatic risk of SLN and NSLN, thereby, classifying patients to relevant axillary management groups. Findings: In the test set, the DLRU yields the best performance in identifying patients with metastatic disease in SLNs (sensitivity=98.4%, 95% CI 96.6-100) and NSLNs (sensitivity=98.4%, 95% CI 95.6-99.9). The DLRU also accurately stratifies patients without metastasis in SLN or NSLN into the corresponding low-risk (LR)-SLN and high-risk (HR)-SLN&LR-NSLN category with the negative predictive value of 97% (95% CI 94.2-100) and 91.7% (95% CI 88.8-97.9), respectively. Moreover, compared with the current clinical management, DLRU appropriately assigned 51% (39.6%/77.4%) of overtreated patients in the entire study cohort into the LR group, perhaps avoiding overtreatment. Interpretation: The performance of the DLRU indicates that it may offer a simple preoperative tool to promote personalized axillary management of breast cancer. Funding: The National Nature Science Foundation of China; The National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; The Scientific research project of Heilongjiang Health Committee; The Postgraduate Research &Practice Innovation Program of Harbin Medical University. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
KeywordDeep learning radiomics Ultrasonography Primary breast cancer Axillary management NSLN metastasis in the axilla
DOI10.1016/j.ebiom.2020.103018
WOS KeywordDISSECTION ; METASTASES ; BIOPSY ; MULTICENTER ; PREDICT ; MODELS ; NOMOGRAM ; OUTCOMES ; DISEASE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81974265] ; National Natural Science Foundation of China[81701705] ; National Natural Science Foundation of China[81630048] ; National Natural Science Foundation of China[81271647] ; National Natural Science Foundation of China[81901761] ; National Outstanding Youth Science Fund Project of National Natural Science Foundation of China[81101103] ; Heilongjiang Provincial Postdoctoral Science Foundation[LBH-Z17174] ; Scientific research project of Heilongjiang Health Committee[2019~050] ; Postgraduate Research &Practice Innovation Program of Harbin Medical University[YJSSJCX2019~08HYD]
Funding OrganizationNational Natural Science Foundation of China ; National Outstanding Youth Science Fund Project of National Natural Science Foundation of China ; Heilongjiang Provincial Postdoctoral Science Foundation ; Scientific research project of Heilongjiang Health Committee ; Postgraduate Research &Practice Innovation Program of Harbin Medical University
WOS Research AreaGeneral & Internal Medicine ; Research & Experimental Medicine
WOS SubjectMedicine, General & Internal ; Medicine, Research & Experimental
WOS IDWOS:000580572100040
PublisherELSEVIER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/42176
Collection中国科学院自动化研究所
Corresponding AuthorTian, Jie; Tian, Jiawei
Affiliation1.Harbin Med Univ, Affiliated Hosp 2, Dept Ultrasound, 246 Xuefu Rd, Harbin, Heilongjiang, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, 95 Zhongguancun East Rd, Beijing, Peoples R China
3.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
4.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian, Shaanxi, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
6.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, Peoples R China
7.Hebei Med Univ, Hosp 2, Dept Gen Surg, Shijiazhuang, Hebei, Peoples R China
8.Harbin Med Univ, Affiliated Hosp 2, Dept MRI Diag, Harbin, Heilongjiang, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Guo, Xu,Liu, Zhenyu,Sun, Caixia,et al. Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer[J]. EBIOMEDICINE,2020,60:11.
APA Guo, Xu.,Liu, Zhenyu.,Sun, Caixia.,Zhang, Lei.,Wang, Ying.,...&Tian, Jiawei.(2020).Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer.EBIOMEDICINE,60,11.
MLA Guo, Xu,et al."Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer".EBIOMEDICINE 60(2020):11.
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