Knowledge Graphs for Cervical Cancer Across the Care Continuum: From Vaccination and Screening to Personalized Therapy.
- 1Department of Health Medicine, Bethune International Peace Hospital, Shijiazhuang, Hebei, 050082, People's Republic of China.
Abstract
Cervical cancer remains one of the leading causes of cancer-related death among women worldwide, with a disproportionately high burden in low- and middle-income countries. Infection with human papillomavirus (HPV) is the central etiological factor. Although HPV vaccination and standardized screening can substantially reduce incidence and mortality, major bottlenecks persist in real-world settings, including inadequate coverage, loss to follow-up, data silos, and limited interpretability of algorithms. Knowledge graphs (KGs), characterized by their core "entity-relation" structure, can integrate electronic medical records, HPV genotyping, pathology/imaging, multi-omics, and public health data within a unified semantic framework, and provide traceable evidence chains and reasoning pathways to support clinical decision-making. KGs may become an important enabler. This review summarizes the potential mechanisms and application paradigms of KGs along the full continuum of cervical cancer prevention and care, including primary prevention (vaccination strategies and health education), screening and diagnosis (multimodal data integration, explainable AI, and clinical decision support systems, CDSS), as well as therapeutic decision-making and follow-up management (molecular subtyping, drug associations, and recurrence monitoring. We further highlight key challenges such as ontology standardization, cross-institutional interoperability, dynamic knowledge updating, and reliability of automated knowledge extraction. Future work should focus on establishing reusable, engineering-oriented pathways for the synergy of "KG + large language models (LLMs)/multimodal AI", the development of explainability evaluation metrics, privacy-compliant frameworks, and deployable solutions for primary care settings, in order to accelerate progress toward the global cervical cancer elimination goal.
Related
- Impact of HPV vaccination on high-risk HPV prevalence in 25-29-year-old women attending cervical screening in England 2016-2025
- Assessing the Capacity of Health Facilities to Deliver Concomitant HPV Vaccination and HPV-Based Cervical Screening Services to Vulnerable Populations – An Implementation Research Protocol
- Optimal timing of HPV vaccination reduces high grade cytology at first cervical screening test
- Optimising HPV Vaccination for Cervical Cancer Prevention in Women Living with HIV: A Cost-Effectiveness Analysis Across 13 African Countries
- Knowledge, attitudes and practices of vaccination among older adults in Brunei Darussalam: a convergent mixed-method study
BibTeX
@article{leng2026knowledge,
title = {{Knowledge Graphs for Cervical Cancer Across the Care Continuum: From Vaccination and Screening to Personalized Therapy.}},
author = {Jiyan Leng and Wenzhi Wei and Xiaohua Wen and Shan Lu and Yanru Liu},
journal = {Therapeutics and clinical risk management},
year = {2026},
month = oct,
doi = {10.2147/tcrm.s619879},
url = {https://europepmc.org/article/MED/42899193},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Knowledge Graphs for Cervical Cancer Across the Care Continuum: From Vaccination and Screening to Personalized Therapy.
AU - Jiyan Leng
AU - Wenzhi Wei
AU - Xiaohua Wen
AU - Shan Lu
AU - Yanru Liu
PY - 2026
DA - 2026/10/15
JO - Therapeutics and clinical risk management
DO - 10.2147/tcrm.s619879
UR - https://europepmc.org/article/MED/42899193
AB - Cervical cancer remains one of the leading causes of cancer-related death among women worldwide, with a disproportionately high burden in low- and middle-income countries. Infection with human papillomavirus (HPV) is the central etiological factor. Although HPV vaccination and standardized screening can substantially reduce incidence and mortality, major bottlenecks persist in real-world settings, including inadequate coverage, loss to follow-up, data silos, and limited interpretability of algorithms. Knowledge graphs (KGs), characterized by their core "entity-relation" structure, can integrate electronic medical records, HPV genotyping, pathology/imaging, multi-omics, and public health data within a unified semantic framework, and provide traceable evidence chains and reasoning pathways to support clinical decision-making. KGs may become an important enabler. This review summarizes the potential mechanisms and application paradigms of KGs along the full continuum of cervical cancer prevention and care, including primary prevention (vaccination strategies and health education), screening and diagnosis (multimodal data integration, explainable AI, and clinical decision support systems, CDSS), as well as therapeutic decision-making and follow-up management (molecular subtyping, drug associations, and recurrence monitoring. We further highlight key challenges such as ontology standardization, cross-institutional interoperability, dynamic knowledge updating, and reliability of automated knowledge extraction. Future work should focus on establishing reusable, engineering-oriented pathways for the synergy of "KG + large language models (LLMs)/multimodal AI", the development of explainability evaluation metrics, privacy-compliant frameworks, and deployable solutions for primary care settings, in order to accelerate progress toward the global cervical cancer elimination goal.
SN - 1176-6336
SN - 1178-203X
ER -