A Knowledge Graph Approach for Breast Cancer Diagnosis and Data Sharing Platform Implementation in the Context of Human Papillomavirus Infection

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Year : 2026 | Volume : 15 | Issue : 01 | Page :
    By

    Xiang Fu,

  • Lin Hu,

  • Xuebei Du,

  1. Researcher, Department of Information Center, Longgang Central Hospital of Shenzhen, Shenzhen city, Guangdong province, China
  2. Researcher, School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, Sichuan province, China
  3. Researcher, Department of Geriatric Infection, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan province, China

Abstract

Background: Breast cancer remains among the most prevalent malignancies in women worldwide, and effective diagnosis and data integration continue to challenge clinical practice. Diagnostic reports from mammography and ultrasound contain rich clinical information that is often under-utilised due to heterogeneous formats and limited data-sharing infrastructure. In the context of human papillomavirus (HPV) infection, which may influence oncogenic pathways and data complexity, advanced computational methods offer new solutions to this problem. Objective: We aimed to construct a comprehensive knowledge graph for breast cancer diagnosis, derived from imaging reports, and to implement a data-sharing platform to enable structured, interoperable data management in a clinical oncology setting. Methods: We collected 1,002 mammography and ultrasound reports and employed a joint entity- and relationship-extraction pipeline using a RoBERTa-Global Pointer model. Model performance metrics accuracy, recall and F1-score exceeded 95%. Extracted information was loaded into a Neo4j knowledge graph containing 23,421 entities and 12,589 relationships. A Spring Boot-based web platform provided interactive visualization, advanced search and intelligent data management of breast cancer and HPV-related diagnostic information. Results: The system demonstrated excellent extraction performance and established a rich semantic network that supports structured data exchange, query and analysis. The platform supports clinicians and researchers in linking imaging features, HPV-related metadata and therapeutic decision-making in a unified data environment. Conclusions: Our knowledge graph and data-sharing platform provide a robust and scalable framework for breast cancer diagnosis and management addressing data-format heterogeneity, enhancing interoperability and enabling precision-driven clinical workflows. This approach offers a generalisable template for other disease domains, particularly where complex imaging and infection-related data converge.

Keywords: Breast cancer, Knowledge graph, human papillomavirus, Breast mammography, Data sharing platform implementation.

[This article belongs to Research and Reviews : A Journal of Medical Science and Technology ]

How to cite this article:
Xiang Fu, Lin Hu, Xuebei Du. A Knowledge Graph Approach for Breast Cancer Diagnosis and Data Sharing Platform Implementation in the Context of Human Papillomavirus Infection. Research and Reviews : A Journal of Medical Science and Technology. 2026; 15(01):-.
How to cite this URL:
Xiang Fu, Lin Hu, Xuebei Du. A Knowledge Graph Approach for Breast Cancer Diagnosis and Data Sharing Platform Implementation in the Context of Human Papillomavirus Infection. Research and Reviews : A Journal of Medical Science and Technology. 2026; 15(01):-. Available from: https://journals.stmjournals.com/rrjomst/article=2026/view=241817


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Regular Issue Subscription Original Research
Volume 15
Issue 01
Received 03/02/2026
Accepted 13/02/2026
Published 29/04/2026
Publication Time 85 Days
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