Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study

doi:10.1016/j.eclinm.2023.102176

科研成果详情

题名	Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study
作者	Chen, Mingzhen 1; Kong, Chunli 1,2,3; Lin, Guihan 1,2,3; Chen, Weiyue1,2,3; Guo, Xinyu 1; Chen, Yaning 1; Cheng, Xue1,2,3; Chen, Minjiang1,2,3; Shi, Changsheng5; Xu, Min1,2,3; Sun, Junhui 4; Lu, Chenying1,2,3; Ji, Jiansong 1,2,3
发表日期	2023-09
发表期刊	EClinicalMedicine 影响因子和分区
语种	英语
原始文献类型	Journal Article
关键词	Breast cancer Convolutional neural network Deep learning Lymph node
摘要	For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images., In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740., For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model., The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively., National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.
资助项目	National Key Research and Development projects intergovernmental cooperation in science and technology of China [2018YFE0126900]; National Natural Science Foundation of China [82072026, 82102162]; Natural Science Foundation of Zhejiang Province [LGF21H180002]; Zhejiang Medical and Health Science Project [2022RC087]
出版者	ELSEVIER
ISSN	2589-5370
EISSN	2589-5370
卷号	63
DOI	10.1016/j.eclinm.2023.102176
页数	13
WOS类目	Medicine, General & Internal
WOS研究方向	General & Internal Medicine
WOS记录号	WOS:001067618500001
收录类别	PUBMED ; SCIE ; SCOPUS
在线发表日期	2023-08
URL	查看原文
PubMed ID	37662514
SCOPUSEID	2-s2.0-85169603386
通讯作者地址	[Sun, Junhui]Division of Hepatobiliary and Pancreatic Surgery,Hepatobiliary and Pancreatic Interventional Treatment Centre,The First Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou,China ; [Lu, Chenying]Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research,Lishui Hospital of Zhejiang University,China ; [Ji, Jiansong]Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research,Lishui Hospital of Zhejiang University,China
Scopus学科分类	Medicine (all)
TOP期刊	TOP期刊
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/182743
专题	其他_附属第五医院（丽水市中心医院）其他_附属第三医院（瑞安市人民医院）
通讯作者	Sun, Junhui; Lu, Chenying; Ji, Jiansong
作者单位	1.Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province,Lishui Hospital,International Institutes of Medicine,School of Medicine,Zhejiaing University,Zhejiang,Lishui,323000,China; 2.Institute of Imaging Diagnosis and Minimally Invasive Intervention Research,The Fifth Affiliated Hospital of Wenzhou Medical University,Lishui,323000,China; 3.Clinical College of the Affiliated Central Hospital,School of Medcine,Lishui University,Lishui,323000,China; 4.Division of Hepatobiliary and Pancreatic Surgery,Hepatobiliary and Pancreatic Interventional Treatment Centre,The First Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou,China; 5.Department of Interventional Radiology,The Third Affiliated Hospital of Wenzhou Medical University,Zhejiang,Ruian,China
推荐引用方式 GB/T 7714	Chen, Mingzhen,Kong, Chunli,Lin, Guihan,et al. Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study[J]. EClinicalMedicine,2023,63.
APA	Chen, Mingzhen., Kong, Chunli., Lin, Guihan., Chen, Weiyue., Guo, Xinyu., ... & Ji, Jiansong. (2023). Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study. EClinicalMedicine, 63.
MLA	Chen, Mingzhen,et al."Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study".EClinicalMedicine 63(2023).