Evidence map›Paper›PMID 40263507›Full record

ArticleScientific reports2025

A comparative analysis of three graph neural network models for predicting axillary lymph node metastasis in early-stage breast cancer.

Enock Adjei Agyekum, Wentao Kong, Yong-Zhen Ren, Eliasu Issaka, Josephine Baffoe, Wang Xian, Gongxun Tan, Chunjing Xiong, Zhangye Wang, Xiaoqin Qian and 1 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Enock Adjei Agyekum *Department of Ultrasound Medicine, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.ORCID http://orcid.org/0000-0003-0021-8555
Wentao Kong *Department of Ultrasound Medicine, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Yong-Zhen RenDepartment of Ultrasound Medicine, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Eliasu IssakaCollege of Engineering, Birmingham City University, Birmingham, B4 7XG, UK.
Josephine BaffoeSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, P.R. China.
Wang XianDepartment of Ultrasound Medicine, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Gongxun TanDepartment of Ultrasound Medicine, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Chunjing XiongNorthern Jiangsu People's Hospital, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, The Yangzhou Clinical Medical College of Xuzhou Medical University, The Yangzhou Clinical Medical College of Jiangsu University, Yangzhou, Jiangsu, China.
Zhangye WangNorthern Jiangsu People's Hospital, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, The Yangzhou Clinical Medical College of Xuzhou Medical University, The Yangzhou Clinical Medical College of Jiangsu University, Yangzhou, Jiangsu, China. domybest612@163.com.
Xiaoqin QianNorthern Jiangsu People's Hospital, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, The Yangzhou Clinical Medical College of Xuzhou Medical University, The Yangzhou Clinical Medical College of Jiangsu University, Yangzhou, Jiangsu, China. yz_tyz1030@126.com.
Xiangjun ShenSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China. xjshen@ujs.edu.cn.

Funding

National Natural Science Foundation of China 82471987National Natural Science Foundation of China 82472004
6 · The paper itself

Abstract

The presence of axillary lymph node metastasis (ALNM) in breast cancer patients is an important factor in deciding whether to have axillary surgery or pursue alternative treatments. Based on axillary ultrasound (US) and histopathologic data, three graph neural network models were compared to predict ALNM in early-stage breast cancer. The patients were randomly divided into two data sets: training (80%) and testing (20%). Predictive performance was measured using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and area under the curve (AUC). In the test cohort, the graph convolutional network (GCN) performed the best in predicting ALNM, with an AUC of 0.77 (95% confidence interval [CI]: 0.69-0.84). In conclusion, the GCN model has the potential to provide a noninvasive tool for detecting ALNM and can aid in clinical decision-making. Prospective studies are expected to provide high-level evidence for clinical usage in future investigations.

Indexed as

Breast NeoplasmsLymphatic MetastasisLymph NodesNeural Networks, ComputerAdultAgedAxillaFemaleHumansMiddle AgedNeoplasm StagingAxillary lymph node metastasisBreast cancerGraph attention networkGraph convolutional networkGraph isomorphism network.Graph neural network

Identifiers

PMID40263507
PMCPMC12015543

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.