Knowledge Graphs for Cervical Cancer Across the Care Continuum: From Vaccination and Screening to Personalized Therapy

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.

Introduction

Human papillomavirus (HPV) infection is the central etiological factor in the development of cervical cancer. Although HPV vaccination and standardized screening can substantially reduce incidence and mortality, persistent bottlenecks such as suboptimal coverage, loss to follow-up, data silos, and limited algorithm interpretability have contributed to the continued high incidence of cervical cancer. This review aims to comprehensively explore the potential roles of knowledge graphs (KGs) in cervical cancer prevention, screening, and personalized treatment, and to analyze the challenges they face, with a view to providing new perspectives and directions for future development in this field. A KG is not a single model, but rather a “semantic substrate” that underpins interoperability and explainable decision-making across the entire continuum of cervical cancer prevention and care; its value depends on ontology standardization, data governance, and the practical implementation of verifiable reasoning chains.

Overview of KGs and Their Applications in Healthcare

With the rapid growth of biomedical information, the potential of KGs in the healthcare domain has expanded explosively, making the effective integration, management, and utilization of heterogeneous data a central challenge. As a structured form of knowledge representation, KGs provide a powerful tool to address this problem.

Definition and Structure of KGs

A KG is a semantic network that represents knowledge in graph form, consisting of nodes (entities) and edges (relations)1 Entities may include diseases, drugs, genes, symptoms, and therapeutic interventions, while relations describe the links between them, such as “drug A treats disease B” or “gene X is associated with disease Y”.1,2 This structured representation enables efficient machine understanding and reasoning over knowledge.3 Wikidata is a large-scale, human-readable, machine-readable, multilingual, and multidisciplinary centralized knowledge base that holds great potential for biomedical data integration and semantic interoperability.2

Key Technologies for KGs Construction

The construction of a KG typically involves information extraction (entity recognition and relation extraction), knowledge fusion, and knowledge storage and management.1 In recent years, the integration of large language models (LLMs) with KGs has become a research hotspot.1,3 Leveraging their powerful natural language understanding and generation capabilities, LLMs can automate entity recognition, relation extraction, and pattern induction, thereby facilitating KG construction.1 Conversely, KGs provide LLMs with structured, explainable factual knowledge, helping to improve factual consistency and reliability in critical domains such as healthcare.3

Applications of KGs in Healthcare

KGs have been widely applied in disease diagnosis, drug discovery, clinical decision support, and personalized treatment recommendation, among other healthcare scenarios.3 For example, by integrating patient medical histories, imaging data, genomic information, and knowledge from the biomedical literature, KGs can assist clinicians in making more accurate diagnoses and treatment plans.4

Applications of KGs in Cervical Cancer Prevention

As a major global public health issue, cervical cancer requires continuous optimization of prevention strategies. With their powerful capabilities for data integration, semantic representation, and reasoning, KGs offer innovative solutions for cervical cancer prevention, particularly in optimizing HPV vaccination strategies, identifying risk factors and stratifying high-risk populations, and enhancing health education and public awareness.

Optimizing HPV Vaccination Strategies

HPV vaccination is the most effective primary prevention measure against cervical cancer.5,6 However, vaccination coverage remains suboptimal worldwide. KGs can provide in-depth, multidimensional analysis to optimize HPV vaccine implementation strategies.

KG-Based Analysis of Vaccine Hesitancy and Refusal

Heterogeneous data from multiple sources—such as social media discussions, posts on health forums, and population-based survey responses—KGs can be used to analyze target groups’ attitudes toward HPV vaccination, identify misconceptions, and characterize relevant sociocultural determinants. Studies have shown that a common misconception is that HPV vaccines are only suitable for females; men have significantly lower awareness of HPV and HPV vaccines than women, and many men perceive HPV vaccination as irrelevant to themselves.7 Moreover, concerns about vaccine safety are widespread. In Kenya, vaccine hesitancy was primarily attributed to safety concerns (76%) and the perception that children were too young to be vaccinated (48%.8 A study in Lebanon found that approximately 73% and 60% of respondents endorsed incorrect beliefs such as “HPV vaccination leads to AIDS” or “HPV vaccines contain harmful substances”.9 KGs can structurally represent these specific misconceptions, knowledge gaps, and associated sociocultural contexts (eg, gender, region, religious taboos), along with information sources (eg, the internet, healthcare professionals, family and friends). By constructing triples that capture “patient characteristics–misconceptions–scientific facts” (for example, <male> – [mistakenly believes] → <HPV infects only women> – [refuted by] → <authoritative guideline>), KGs can provide an evidence base for subsequent stratified health communication and intervention evaluation. However, this approach still requires more real-world, cervical-cancer–specific validation studies.

Recommending Risk Factor–Driven Personalized Vaccination Schemes

By integrating epidemiological data, individual sexual behavior characteristics, family history, geographic factors, and sociodemographic information, KGs can support the construction of individual risk assessment models to generate personalized HPV vaccination recommendations. Multiple studies have shown that sociodemographic characteristics (eg, educational level, income, gender, age), health behaviors, and perceived risk are significantly associated with vaccination intention.7,10–12

KGs can identify region-specific and culturally specific determinants and thereby support the optimization of HPV vaccine delivery models, including the configuration and promotion of vaccination services, to improve accessibility and acceptance.8 Through semantic representation and linkage of these multidimensional data, KGs can deliver tailored HPV vaccine information and vaccination schedules based on individual profiles, thus supporting personalized HPV vaccination recommendations.

Strategy Optimization to Accelerate Vaccine Uptake

Evidence indicates that effective recommendations from healthcare professionals are among the most important predictors of HPV vaccination and can markedly increase the likelihood of vaccine uptake.13 Physician counseling and education significantly improve public knowledge about HPV and comfort with discussing HPV, and markedly increase vaccination volume in the corresponding clinics.14 In addition, exposure to information is a positive predictor of HPV-related knowledge, and the internet, school-based lectures, and interpersonal influence are key channels for information dissemination.7 Barriers to vaccination include cost, accessibility, safety concerns, and insufficient information.7–9

KGs can integrate these interventions, influencing factors, and outcome data to build a dynamic model. System-level, integrative analyses can be used to identify the most effective intervention pathways—for example, strengthening training of healthcare providers in cervical cancer–related knowledge, leveraging social media for targeted science communication,15 or implementing free or subsidized vaccination programs in cost-sensitive settings.9 Such optimized strategies can maximize HPV vaccination coverage.

Risk Factor Identification and High-Risk Population Stratification

The development of cervical cancer is driven by a combination of factors. KGs can effectively integrate these complex data to enable accurate identification and stratified management of high-risk populations.

Integrating Multidimensional Risk Factors

By integrating sociodemographic characteristics (eg, education, income), lifestyle factors (eg, smoking, sexual behavior patterns), genetic susceptibility, and environmental exposures, KGs can support the construction of comprehensive cervical cancer risk assessment models. Prior studies have demonstrated that sociodemographic variables such as age, gender, education level, income, race/ethnicity, occupation, and geographic location are associated with HPV infection risk, awareness of cervical cancer, and adherence to vaccination.7,12 Furthermore, sexual behavior patterns (eg, multiple sexual partners, unprotected sex), HIV status, chronic comorbidities, and mental health are also critical for risk assessment.8,16 By structurally representing these entities and their interrelations, KGs can support a comprehensive, dynamically updated cervical cancer risk assessment system that reveals complex associations among diverse risk factors and thereby enables more precise risk estimation at both the individual and population levels.

Analyzing the Impact of Sociocultural Factors on Preventive Adherence

Sociocultural determinants have a pronounced influence on adherence to cervical cancer preventive behaviors. KGs can be used to explore how educational attainment, economic status, community support, religious beliefs, and other social determinants affect adherence in specific groups. In South Korea, cultural taboos surrounding discussions of sexuality and traditional gender norms may hinder mothers’ recognition of the seriousness of HPV infection in males.15 Among specific groups such as African American women, cervical cancer incidence and mortality are substantially higher than in other ethnic groups, potentially reflecting deeper social factors such as mistrust of the healthcare system and cultural barriers.16 KGs can model the relationships between such sociocultural factors (eg, language barriers, cultural beliefs, historical mistrust) and preventive adherence in particular ethnic or social groups. Semantic analysis of these complex relationships can help to clarify determinants of adherence to cervical cancer prevention measures.

Integrating Geographic Information Systems (GIS) with KGs

The integration of geographic information systems (GIS) with KGs can provide spatial support for cervical cancer prevention. KGs can integrate geospatial data—such as the distribution of healthcare facilities, transportation accessibility, and regional socioeconomic indicators—and link them with epidemiological data on cervical cancer incidence, screening coverage, and vaccination rates. Studies have shown marked regional disparities (eg, urban vs rural, differences across cities) in knowledge levels, vaccination coverage, and healthcare resources.8,13 By combining GIS with KGs, it is possible to identify geographic clusters with elevated cervical cancer risk and “health desert” areas with poor access to medical services, thereby enabling more efficient and equitable targeting of preventive interventions.

Enhancing Health Education and Public Awareness

Public knowledge and awareness of cervical cancer and its prevention directly affect adherence to preventive measures. KGs can transform health education paradigms by identifying and correcting public misconceptions, thereby improving awareness. Using natural language processing (NLP) and KG technologies, large volumes of information from social media, health forums, and other online platforms can be analyzed in real time to identify common misconceptions and misinformation regarding HPV and cervical cancer. Typical misunderstandings include concerns about vaccine safety, erroneous associations between vaccination and sexual behavior, and the belief that vaccines are only for women.7–9,11

The Role of KGs in Cervical Cancer Screening

Cervical cancer screening is crucial for early detection, early diagnosis, and early treatment. The overall architecture of a KG for the full cervical cancer control continuum can be conceptualized as a four-layer structure from bottom to top. The data layer aggregates multi-source, heterogeneous data covering the entire clinical pathway and public health dimensions. The semantic layer employs domain ontologies (eg, HPV genotyping schemes, disease staging standards) to normalize these data into a unified conceptual framework. The core KG layer then builds a computable network based on an entity–relation–evidence-source model, supported by an inference engine. Finally, the application layer leverages KG application programming interfaces to support a wide range of downstream tasks, from individual-level clinical decision support (clinical decision support systems and explainable AI) to population-level resource optimization Figure 1).

Figure 1.

The overall architecture of the full-chain Knowledge Graph (KG) for cervical cancer prevention and control. Four-layer knowledge graph (KG) architecture for cervical cancer prevention and care. From bottom to top: data layer (multi-source clinical, imaging, HPV genotyping, omics, and public health data); semantic layer (domain ontologies and normalization); core KG layer (entity–relation–evidence network with inference engine); application layer (individual clinical decision support, explainable AI, and population-level resource optimization). Tool: The figure was created in draw.io (diagrams.net) and refined in Adobe Illustrator.

Intelligent Auxiliary Diagnosis and High-Risk Population Identification

KGs can construct a comprehensive, multidimensional knowledge view of each patient, thereby providing a robust data and knowledge foundation for AI applications in cervical cancer screening and enabling more accurate auxiliary diagnosis and identification of high-risk individuals.

Integration of Multimodal Screening Data

Cervical cancer screening involves multiple heterogeneous data sources, including cytology, HPV DNA test results, pathology reports, colposcopic images, clinical signs and symptoms, and patients’ medical histories. KGs can establish a unified semantic framework to integrate these data from different modalities and sources, forming a comprehensive and structured knowledge system for cervical cancer screening.17,18 For instance, although convolutional neural networks (CNNs) perform excellently in image recognition, they generally lack pathological interpretability. By mapping image-derived features (eg, “increased nuclear-to-cytoplasmic ratio”) to concepts in a pathology ontology (eg, “features of HSIL”), KGs can support the construction of neuro-symbolic systems. This not only improves diagnostic accuracy but, more importantly, endows AI models with clinically meaningful interpretability aligned with pathological reasoning.

Biomarker-Integrated Risk Stratification

By linking genomic data (such as HPV genotypes, host genetic polymorphisms, and DNA methylation patterns), proteomic profiles, and microbiome information, KGs can support more precise assessment of the risk that precancerous lesions—such as cervical intraepithelial neoplasia (CIN)—will progress to invasive cancer.19 The development of risk stratification models for CIN2 or higher-grade lesions (CIN2+), based on the integration of multi-omics data—particularly HPV integration status, somatic mutations, and copy number alterations—combined with machine learning algorithms, has become an important direction in cervical cancer research.20–22 Using capture sequencing and machine learning based on HPV and human integration genomic profiles, Tian et al successfully stratified the risk of cervical lesions.23 KGs can structurally represent these biomarkers, their biological functions, and their causal relationships with disease progression, thus providing richer biological context for risk prediction models. In addition, the combination of generative AI and language models can, through data augmentation and natural language processing techniques, transform tabular patient data into rich textual descriptions, which has been shown to substantially improve classification accuracy for cervical cancer and to further support fine-grained management of high-risk populations by clinicians.24 A detailed summary of related work is provided in Table 1.

Table 1.

Applications of Different AI Technologies for Risk Stratification Supported by Knowledge Graphs

AI Technology / KG-Enabled Approach	Data Integration	Key Functions	Clinical Value
Multi-omics–based risk models	Genomic data (HPV genotypes, somatic mutations), clinical data	Construction of risk prediction models using machine learning algorithms	Accurate prediction of CIN2+ progression risk and realization of individualized risk stratification22
Generative AI and language models	Multidimensional tabular patient data	Transformation of structured data into textual descriptions for NLP-based classification	Improvement of cervical cancer classification accuracy through data augmentation and semantic understanding24
Causal inference models	Cytopathological images of cervical cells	Integration of causal discovery, causal reasoning, and deep learning models to uncover latent causal relationships	Enhancement of diagnostic accuracy for cervical lesions and improvement of model interpretability25

Explainability and Validation of AI-Assisted Diagnostic Systems

KGs provide a structured knowledge backbone for AI-assisted diagnostic models such as deep learning, thereby substantially enhancing model explainability (explainable AI, XAI).26,27 By linking AI-generated diagnostic outputs to medical concepts, disease progression pathways, and guideline-based recommendations encoded in the KG, clinicians can clearly understand the rationale behind AI decisions instead of merely receiving a numeric prediction. In one study, the diagnostic accuracy of gynecologists for CIN2–3 and invasive cancer increased from 54.4% to 58.0% and from 38.9% to 48.5%, respectively.28 Although challenges such as data standardization and model validation remain before AI can be widely adopted in routine clinical practice, the introduction of KGs is expected to mitigate these issues by providing transparent decision pathways and standardized data frameworks.29

Optimization of Screening Workflows and Quality Management

As a powerful engine for knowledge management and reasoning, KGs can play a pivotal role in standardizing cervical cancer screening workflows, optimizing resource allocation, and enabling continuous quality management.

Support for Clinical Decision Support Systems (CDSS)

KGs represent an ideal backend for building efficient clinical decision support systems (CDSS). They can formalize and structure the latest national and international guidelines for cervical cancer screening and management, and translate them into computer-executable rules and logic.30 Based on this guideline knowledge and the integrated, patient-specific clinical data, CDSS can provide real-time, personalized decision support for gynecologists and preventive care physicians, including recommendations on screening intervals, management of abnormal results, diagnostic workup pathways, and follow-up plans. In this way, CDSS help ensure that clinical practice remains aligned with best available evidence, streamline diagnostic and treatment workflows, and significantly improve patient follow-up rates.31,32

Continuous Improvement of Screening Quality

KGs play a key role in quality management and continuous quality improvement (CQI) of cervical cancer screening. A mixed-methods study on CDSS applications found that, despite reasonable tool design, the adoption rate in community clinics was only 41%, largely due to limited awareness of the tool’s existence and functions, as well as lack of standardized training.33 By constructing a knowledge base covering tool functionalities, usage workflows, and best practices, KGs can provide context-aware training and guidance, thereby improving both adoption and effective use of such tools. Furthermore, through integration and analysis of data across the entire cervical cancer screening continuum, KGs can quantitatively evaluate the impact of quality improvement measures, offering strong data and knowledge support for all CQI steps, including “problem identification”, “root cause analysis”, “development of potential solutions”, and “evaluation of outcomes”.

Triage, Referral, and Treatment Coordination for Screen-Positive Populations (KG-Driven Closed-Loop Management)

Once high-risk individuals have been identified through cervical cancer screening, the key challenge is how to accurately guide these patients toward individualized treatment and to optimize therapeutic strategies, thereby improving prognosis and quality of life. In the “screening-to-treatment” transition, KGs are increasingly serving as a critical bridge: they transform the rich information accumulated during the screening phase into actionable knowledge that supports treatment decision-making, enabling a seamless transition from “problem detection” to “problem resolution”.

The diagnosis and treatment process for high-risk cervical cancer patients involves massive amounts of heterogeneous data, including genomic profiles, histopathology reports, clinical histories, prior treatment responses, and even geographical and socioeconomic factors. KGs can establish a unified semantic framework that aggregates these fragmented data sources into a comprehensive, structured patient knowledge view.34 Moreover, through data linkage and graph-structuring methods, KGs can effectively identify and mine potential, often overlooked risk factors embedded in electronic health records, thereby improving the accuracy of cervical lesion progression prediction and providing a more solid basis for subsequent treatment choices.34 By organizing and enriching cancer registry data, KGs also accelerate researchers’ understanding of cancer incidence patterns, disparities, and outcomes, thus providing a stronger data and knowledge foundation for treatment decisions in high-risk patients.35 This comprehensive data integration capability enables treatment decisions for high-risk populations to be made on richer and more precise information.

Building upon this integrated data foundation, KGs further empower the development of personalized treatment strategies for cervical cancer. By combining patients’ unique molecular profiles, disease progression patterns, and potential responses to various therapeutic options, KGs can assist clinicians in designing highly customized treatment plans. For patients with locally advanced cervical cancer, KGs coupled with deep learning models (eg, spatial and task attention networks) can automatically segment tumor regions and predict radiotherapy response, thereby guiding the design of precise, individualized radiotherapy regimens.36 In parallel, by constructing gene signature models based on immune infiltration, KGs can help predict prognosis and immunotherapy responsiveness in cervical cancer, providing new avenues for improved patient management and personalized treatment.37 Importantly, through knowledge graph embeddings and semantic inductive reasoning, KGs can generate patient-specific treatment recommendations from large-scale clinical trial data, underpin clinical trial design with robust evidence, and accelerate the translation of novel therapies into cervical cancer practice.38

The Role of KGs in Personalized Treatment of Cervical Cancer

Personalized treatment of cervical cancer aims to provide the most effective therapy with minimal toxicity based on the patient’s unique biological characteristics. With the rapid development of multi-omics technologies and big data analytics, KGs—capable of integrating vast heterogeneous biomedical information and uncovering deep relationships—have become a powerful enabler of personalized therapy in cervical cancer. Systems such as the Scalable Precision Medicine Open Knowledge Engine (SPOKE),39 the multi-relational KG AIMedGraphh,40 and the multimodal KG PrimeKG41 exemplify representative design concepts and functionalities, and offer important references for understanding the application of KGs in this field.

Molecular Subtyping and Precision Targeted Therapy

Precision targeted therapy is one of the core strategies of personalized treatment for cervical cancer. By integrating multidimensional data, KGs can comprehensively characterize the molecular features of tumors to guide precise therapeutic interventions. Specifically, KGs can deeply integrate genomic sequencing, transcriptomic, proteomic, and metabolomic data from cervical cancer patients, together with clinicopathological characteristics, prior treatment histories, and imaging data. This enables the construction of individualized disease graphs that provide a holistic depiction of the tumor’s molecular biology. Representative KGs and graph-based models and their key characteristics, summarized from current literature, are presented in Table 2.

Table 2.

Characteristics of Representative KGs and Graph-Based Models

Resource/MODE	Type	Core Characteristics	Key Functions	Association with Cervical Cancer
P-NET	Graph model (non-KG Biologically Prior-Driven Explainable Neural Networks	Biologically prior-driven interpretable neural network)	Queue-based discovery; Clinical–Molecular Cross-Domain Association	Graph-based integration and population characteristic analysis for screening–diagnosis–treatment data.42
KG4SL	Graph Neural Network (Non-KG)	Graph Attention Network for Tumor Synthetic Lethality Prediction	Integrating background data (eg, gene, disease, and pathway data) for SL gene prediction; identification of potential therapeutic targets	Can be used to identify candidate SL genes associated with HPV-driven tumors (at the research exploration stage).43
DeepCDR	Mixed Graph Convolutional Model (Non-KG)	Combined modeling of pharmaceutical chemical structures and tumor cell line omics characteristics	Drug response prediction (eg, IC50); Drug resistance mechanism analysis	Drug sensitivity prediction and treatment regimen optimization for cervical cancer cell lines (preclinical setting).44
Hetionet	KG	Multiple types of nodes (eg, diseases, genes, biological pathways, drugs, symptoms, etc) and heterogeneous relationships	Link prediction; Drug repurposing	Cross-domain evidence integration supports the generation of hypotheses and the linkage of evidence around HPV-related pathways and candidate drugs.45
PrimeKG	KG	Disease–Phenotype–Drug Heterogeneous Large-Scale Graph.	Supports multi-hop reasoning for drug repurposing	The mechanistic pathway interpretation can be used to explore potentially repurposable drugs and mechanistic hypotheses for cervical cancer subtypes (transferable to general oncology settings).41

Applications of Knowledge Graphs in Precision Immunotherapy and Whole-Course Management of Cervical Cancer

As a core technology for integrating multimodal biomedical data, KGs are reshaping precision diagnosis and treatment paradigms for cervical cancer. By deeply integrating genomic, transcriptomic, and clinical phenotypic data, KGs show substantial translational potential in predicting responses to immunotherapy, optimizing combination regimens, and enabling dynamic prognostic stratification.

Tumor Microenvironment Characterization and Prediction of Immunotherapy Response

Cervical cancer patients exhibit marked heterogeneity in response to immune checkpoint inhibitors (eg, PD-1/PD-L1 monoclonal antibodies), largely driven by the complex interactions within the tumor immune microenvironment (TME). Leveraging KGs to integrate multidimensional data is a key approach to addressing this challenge. Authoritative graphs such as PrimeKG,41 which integrates over 100,000 nodes and millions of relations, can systematically link tumor mutational burden (TMB), HLA types, and immune cell infiltration patterns within the TME. Such high-dimensional network analyses facilitate the identification of critical immune escape pathways and potential biomarkers. For example, based on the SPOKE39 architecture, researchers can map HPV integration site data onto host gene regulatory networks to accurately predict virus-driven antigen presentation patterns. Compared with traditional single biomarkers, network topology–based predictive models can more precisely estimate a patient’s sensitivity to immunotherapy and assist in selecting those most likely to benefit.

Intelligent Optimization of Combination Therapy Strategies and Toxicity Control

To overcome therapeutic resistance in cervical cancer, rational combination therapies (eg, immunotherapy combined with chemoradiotherapy or targeted agents) are crucial. KGs offer unique advantages in uncovering drug synergy mechanisms and predicting adverse drug reactions (ADR). The Clinical Knowledge Graph (CKG)46 and similar architectures construct multilayer “drug–target–pathway–disease” networks that can simulate the perturbation effects of different drug combinations on tumor signaling pathways. This facilitates the discovery of repurposed drug combinations with synergistic effects and, by applying graph neural network (GNN) algorithms, enables prediction of potential drug–drug interactions (DDI). For instance, the KGDN (Knowledge Graph Neural Network) model47 can effectively predict candidate DDIs and outperforms several classical models in this task.

Dynamic Prognostic Assessment and Relapse Risk Monitoring

Long-term survival management of cervical cancer hinges on accurate prognostic stratification and real-time monitoring. In contrast to traditional FIGO staging, KGs support dynamic prognostic models that incorporate multi-omics profiles and longitudinal clinical trajectories. By integrating electronic health records (EHRs) with omics data, KGs can delineate a “digital twin” trajectory for each patient. Leveraging the multimodal reasoning capabilities of PrimeKG or Hetionet,48 models can dynamically link pathological subtypes, molecular features (eg, PIK3CA mutation status), and imaging changes observed during follow-up. This dynamic updating mechanism allows the system to detect relapse risk signals before overt clinical symptoms appear. In addition, deep learning–based sequence models can be embedded within the KG framework to continuously learn from longitudinal care data, providing personalized early warning for high-risk patients and enabling timely intervention within the optimal therapeutic “window”.

Challenges of KGs in Cervical Cancer Prevention and Control

Although KGs can integrate information across prevention, screening, and individualized treatment of cervical cancer, their large-scale deployment and maximal effectiveness are still constrained by multiple challenges, spanning technical, managerial, and infrastructural domains.

Bottlenecks in Data Interoperability and Standardization

Data sources in cervical cancer prevention and control are highly diverse, including clinical records, pathology reports, medical imaging, genomic data, and public health surveillance data. Integrating such heterogeneous data into a unified KG poses substantial challenges in terms of interoperability and standardization.

Heterogeneous Data Integration and Semantic Interoperability

Comprehensive cervical cancer control strategies require the integration of heterogeneous data from clinical, pathological, imaging, genomic, and public health domains. These data often vary in format and terminology standards, creating significant barriers to data integration and semantic interoperability in KG construction.49 Biomedical data frequently exist in unstructured (eg, text, audio, video) and heterogeneous forms, and their diversity and complexity are major obstacles to integration.50 A cervical cancer–specific challenge lies in inconsistent pathology reporting standards (eg, mixed use of the CIN grading system and LAST terminology). KG construction must address how to accurately map terms such as “CIN2”, “CIN3”, and “HSIL” across ontologies to ensure semantic consistency of data from different institutions. Effective ontology matching therefore becomes a key requirement.51

Complexity of Data Cleaning, Ontology Mapping, and Knowledge Alignment

Building a high-quality KG, particularly for cervical cancer prevention and control, requires rigorous data cleaning and accurate ontology mapping and knowledge alignment across data sources. One study proposed a smart ontology alignment (SOA) method based on “knowledge units” and “distinctive features”, which distinguishes between unique and defeasible attributes of concepts and can significantly improve alignment accuracy and efficiency.52 In the context of cervical cancer, this implies close collaboration among pathologists, clinicians, epidemiologists, and information technology experts to define cervical cancer–related ontologies and ensure that diverse sources—such as TNM staging systems, HPV genotyping, and treatment guidelines—are correctly mapped to corresponding KG concepts while preserving semantic consistency.53

Efficiency of Knowledge Acquisition and Updating

Medical knowledge in the cervical cancer domain is evolving rapidly, with continuous advances in disease mechanisms and diagnostic and therapeutic strategies. KGs must therefore be capable of efficiently extracting and integrating new knowledge from large volumes of unstructured literature to maintain timeliness and accuracy. However, current knowledge acquisition and updating processes still face significant bottlenecks.

Rapid Evolution of Medical Knowledge

New findings, clinical trial results, and guideline updates are constantly emerging—for example, revisions to cervical cancer screening guidelines, development of novel HPV vaccines, and advances in individualized treatment strategies—which all need to be promptly incorporated into KGs. Yet existing KGs, especially those curated manually by experts such as Gene Ontology (GO) annotations, often require “extensive expert curation”. This labor-intensive process limits the speed at which KGs can be updated, potentially lagging behind the pace of medical innovation.54 Although LLMs can acquire knowledge from large-scale text corpora, in medicine they often struggle with complex domain-specific conceptual relationships and may fail to infer deeper connections among concepts. KGs, as structured repositories, can explicitly store rich factual knowledge and capture nuances in complex data, thereby providing complementary information to LLMs for tasks requiring structured understanding. Some studies have integrated KG embeddings (KGEs) into LLMs via lightweight mapping networks, enabling parameter-efficient use of external knowledge and seamless handling of out-of-vocabulary terms. This is particularly important for efficiently incorporating and updating new knowledge in KGs within a rapidly evolving medical environment.49

Challenges in Automated Knowledge Acquisition and Validation

Despite their strong performance in knowledge extraction, LLMs in medicine still face issues of factual accuracy and “hallucination”, which must be constrained and validated using the structured knowledge in KGs. LLMs are challenged by complex reasoning, reliability, and factual consistency, all of which are critical in high-stakes domains such as healthcare.49

Although NLP methods can be used to build KGs from documents, they are “never perfect”, and the efficiency challenge of automated knowledge acquisition is further reflected in the time-consuming tasks of constructing training corpora and high-performing models.53 Studies integrating KGEs with LLMs have shown that LLMs benefit substantially from the factual grounding provided by KGEs.49 In addition, interpreting the internal knowledge of LLMs through KGs can enhance validation and trust in automatically acquired knowledge, and quantitative metrics derived from KGs can be used to diagnose strengths and weaknesses in model behavior, thereby guiding model refinement.55

Privacy, Security, and Regulatory Compliance KGs for Cervical Cancer Must Comply with Regional Regulations and Institutional Policies. Recommended Patterns Include

Federated or hybrid architectures to minimize data movement; role-based access controls and audit trails. Pseudonymization and differential privacy for secondary analytics; data use agreements aligned to consent. Clear data provenance in KGs to resolve knowledge conflicts and enable rollback.

Computational and Integration Requirements Infrastructure

Scalable storage/compute for graph databases and inference; MLOps/DevOps for versioning and monitoring. EHR integration: standards-based interfaces (eg, FHIR/terminology services) and computable guideline engines.30 Explainability and safety: quantify fidelity of paths, calibration of risk scores, and out-of-distribution detection in KG-augmented models.29

Cost Barriers and Clinician Adoption

Costs include data standardization, ontology curation, IT integration, and training. Phased pilots with ROI tracking are advisable. Adoption hinges on usability, transparency, and fit to clinic workflows; KG-backed training content can increase awareness and effective use.33

Future Directions for KGs in Cervical Cancer

As a novel technology capable of semantically representing, integrating, and reasoning over heterogeneous data, KGs have demonstrated tremendous potential in medicine, particularly in whole-course management of cervical cancer prevention, screening, and individualized treatment. To fully unleash the value of KGs in cervical cancer control, future research should advance in the following areas:

Deep Integration of Large Language Models (LLMs)

Developing synergistic frameworks that combine LLMs with KGs will be crucial. The powerful text understanding and generation capabilities of LLMs can be leveraged to automatically extract entities and relations from large volumes of unstructured medical literature, EHRs, and clinical trial reports, thereby enabling rapid construction, dynamic updating, and content expansion of KGs. Conversely, the structured, validated medical facts encoded in KGs can serve as a “knowledge substrate” for LLMs, constraining their outputs, reducing hallucinations, and ensuring reliability and trustworthiness in use cases such as medical consultation, report generation, and clinical decision support.56 Recent findings indicate that proprietary LLMs, particularly ChatGPT-4.0 Turbo and Claude 2, show promise in clinical decision-making tasks involving logical reasoning, suggesting that LLMs may become effective tools to assist clinicians in cervical cancer–related decisions.57

Cross-Modal Data Integration and Explainable AI (XAI)

Future research will focus on deeper cross-modal integration, seamlessly incorporating medical imaging (eg, colposcopy, whole-slide pathology images), clinical text, genomics, proteomics, metabolomics, and other data into a unified KG framework.58,59 In parallel, developing more transparent and interpretable AI models is essential. KG-driven decision systems should not only provide “what” recommendations but also explain “why”. By exposing the underlying reasoning pathways and key evidence (for example, a knowledge path connecting a specific gene mutation, signaling pathway, and drug target), such systems can substantially enhance clinicians’ trust in and adoption of AI-assisted decision-making.60

Deployment in Primary Care and Public Health

To address global disparities in healthcare resources, especially in low- and middle-income countries, there is an urgent need for lightweight, easily deployable, and user-friendly KG solutions. These can be integrated with mobile health and telemedicine technologies to empower primary healthcare workers in resource-limited settings. For example, KG-enabled mobile applications could provide frontline clinicians with standardized screening workflow guidance, risk assessment tools, and initial diagnostic and management suggestions, while also delivering accessible health education content to the public. Such tools can help narrow the digital divide and improve equity and accessibility in cervical cancer prevention and control.61

Constructing a Cervical Cancer “Digital Twin”

Integrating life-course data from birth to old age to construct an individualized cervical cancer “digital twin” model represents a long-term vision for achieving ultimate precision medicine.62 This model would integrate an individual’s genetic background, lifestyle, environmental exposures, vaccination history, screening results, diagnostic details, treatment regimens and responses, and follow-up outcomes. KGs would play a central role in data integration and reasoning, enabling the digital twin to dynamically simulate disease trajectories, accurately predict individual responses to different interventions, and continuously optimize prevention and treatment strategies in real time.

Strategic Value of KGs in Accelerating Cervical Cancer Elimination

As a bridge connecting large-scale heterogeneous medical data, domain knowledge, and advanced AI-based decision-making, KGs have strategic value in fundamentally enhancing the precision and intelligence of the entire cervical cancer care continuum—from prevention and screening to treatment. By enabling targeted health education to address vaccine hesitancy, optimizing screening workflows for earlier detection, and guiding individualized therapies to improve outcomes, KGs are poised to become a key technical pillar supporting the WHO’s global strategy to “accelerate the elimination of cervical cancer”. As technologies mature and applications deepen, KGs will drive a shift in cervical cancer control from macro-level population strategies to micro-level individualized precision management, ultimately providing strong momentum toward the ambitious goal of a “cervical cancer–free future”.

Conclusion and Perspectives

KGs can help unify heterogeneous data and encode cervical cancer guidelines for transparent, auditable decisions from prevention through follow-up. However, real-world value depends on rigorous ontology alignment, privacy-preserving architectures, robust EHR integration, explainability metrics, and clinician-centered design. Practical next steps include: (1) a minimal viable cervical cancer ontology harmonizing HPV genotypes and CIN/LAST terms; (2) computable-guideline pilots that close screening-to-diagnostic loops with measurable reductions in missed follow-up; and (3) validated KG–LLM toolchains with governance to manage knowledge conflicts and updates. With disciplined engineering and evaluation, KGs may meaningfully enhance precision and equity in cervical cancer care.

Methods for Literature Identification

Databases and timeframe: PubMed/MEDLINE, Web of Science, IEEE Xplore, arXiv; January 2010–December 2025 (English-language focus; select global public health reports and standards where relevant).

Search terms: combinations of “cervical cancer” AND “knowledge graph” OR “semantic graph” OR “ontology” OR “clinical decision support” OR “explainable AI” OR “HPV vaccination” OR “screening” OR “colposcopy” OR “genomics” OR “immunotherapy”.

Inclusion criteria: studies, systems or frameworks that explicitly construct or leverage KGs; or graph-based methods demonstrably integrated with KGs; cervical cancer–specific applications prioritized; when illustrating transferable techniques, oncology exemplars were included with explicit caveats.

Exclusion criteria: purely descriptive graphs without clinical or methodological relevance; non-graph AI papers without a KG component unless directly cited to contextualize cervical cancer–specific use.

Study selection and quality considerations: titles/abstracts screened by two authors; full-text appraisal emphasized clarity of KG construction/use, data sources, and evaluation. Given heterogeneity and the review nature, formal risk-of-bias scoring was not feasible; instead, we qualitatively assess data provenance, transparency, and external validity where available.

Funding Statement

Medical Science Research Project of Hebei Health Commission (Grant No. 20220272).

Disclosure

The authors report no conflicts of interest in this work.