Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
| 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
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| Source Publication | EBIOMEDICINE
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| ISSN | 2352-3964 |
| 2020-10-01 | |
| Volume | 60Pages:11 |
| Corresponding Author | Tian, Jie(jie.tian@ia.ac.cn) ; Tian, Jiawei(jwtian2004@163.com) |
| Abstract | Background: 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/) |
| Keyword | Deep learning radiomics Ultrasonography Primary breast cancer Axillary management NSLN metastasis in the axilla |
| DOI | 10.1016/j.ebiom.2020.103018 |
| WOS Keyword | DISSECTION ; METASTASES ; BIOPSY ; MULTICENTER ; PREDICT ; MODELS ; NOMOGRAM ; OUTCOMES ; DISEASE |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National 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 Organization | National 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 Area | General & Internal Medicine ; Research & Experimental Medicine |
| WOS Subject | Medicine, General & Internal ; Medicine, Research & Experimental |
| WOS ID | WOS:000580572100040 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/42176 |
| Collection | 中国科学院分子影像重点实验室 |
| Corresponding Author | Tian, Jie; Tian, Jiawei |
| Affiliation | 1.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 Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute 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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