We propose a methodology primarily inspired by Neon [37] and Systematic Approach for Building Ontologies (SABiO) [10] methodologies. While the former supports the reuse and restructure of ontological sources, knowledge integration, and collaboration in building evolving ontology networks, the latter focuses on a step-by-step process, ontological analysis, requirement traceability, and validation for developing domain ontologies. Both methods commonly initiate the development process with requirements specification and the use of competency questions (CQs) for functional requirements. Given the heterogeneity, complexity, and evolving nature of the application domain, we combined these methods into a single iterative hybrid methodology. The novelty lies in combining SABiO’s structured development, focused on ontological analysis and validation, with Neon’s dynamic scenario-based process, focused on reuse, mapping, and evolution. This combination creates a step-by-step, detailed methodology that is requirement-driven yet systematically and formally well-founded, thereby simplifying hyperontology development while maintaining semantic interoperability. Our methods integrate the metadata collected from heterogeneous data models and their associated semantic mappings with conceptual unpacking [38,39] and oncology modeling based on mCODE specifications [28,29]. Support processes, including ontology layering and modularization, have also been used to streamline the hyperontology development. Additionally, we have held regular meetings and discussions with EUCAIM partners and medical experts throughout the development and actively incorporated their feedback into the framework.

We initiated the ontology development by analyzing the ontological requirements and specifications (including purpose, scope, users, and uses). The functional requirements that specify the knowledge the hyperontology should represent are stated as CQs, classified by cancer type. We used a bottom-up approach to syntactically analyze the AI4HI collected data and formulate 285 knowledge-based, specific CQs and answers. Furthermore, these questions are generalized into 28 generic CQs (Textbox 1). Identifying CQs was an effective way not only for ontology scoping—that is, identifying ontologically relevant content—but also for capturing interactions, detecting inconsistencies and information gaps, including nonstandardized data, and evaluating the ontology. Additionally, nonfunctional requirements that address generic qualities and capabilities, such as FAIR compliance and language support, have been considered. Regarding the specifications, the hyperontology seeks to integrate and harmonize heterogeneous data by providing an unambiguous and faithful representation of oncology and medical imaging. It covers AI4HI and new datasets emerged from new data holders joining the EUCAIM community. Finally, the hyperontology will support the exploration of data collections, image annotation and segmentation, as well as federated querying and processing performed by data users or researchers. The Ontology Requirements Specification Document (ORSD) [40], initially proposed in Neon [37], illustrates all these requirements and specifications.

After specifying the ontological requirements, we began harvesting the knowledge needed to construct the hyperontology’s content and structure, primarily through mappings to standard biomedical terminological and ontological resources (eg, SNOMED-CT, LOINC, ICD-10, ICD-O-3, RadLex, and NCIT). We revised the collected knowledge in the ORSD to verify any incomplete information, such as missing labels, vocabulary, or code, and to prevent information loss. Furthermore, 2 main types of mappings have been applied: hierarchical (is-a), which focuses on harvesting taxonomic relations, and exact match mappings, which concentrate on alternative labels, codes, and definitions (Figure 1).

Additionally, considering the interactions captured from CQs, we identified semantic relationships across categories, accounting for their necessity and consistency to prevent the ontology from becoming excessively complex or extensive. For instance, “Has associated morphology” is an object property defined in SNOMED-CT and helps connect cancer conditions (eg, Malignant tumor of breast (SNOMED-CT, 254837009)) with their related histology and morphology (eg, Malignant Neoplasm (SNOMED-CT, 367651003)). Tumor morphology, which AI4HI did not commonly provide, is also collected from ICD-O-3 and SNOMED-CT to enrich and maintain this content, given its querying requirements and importance in supporting cancer diagnosis. For imaging, we mapped DICOM metadata to RadLex, a primary radiology standard recommended by experts. We also aligned the imaging values (labels) in the DICOM metadata with various standards (eg, SNOMED-CT and RadLex) using an exact-match similarity approach.

The mappings are performed automatically across multiple resources that combine many health and biomedical vocabularies and standards.:

Finally, we revised the results to detect differences or inconsistencies and manually curated them to remove any information irrelevant to the application domain. The curation process was challenging due to the complexity and heterogeneity in how concepts are represented and specified across standard terminologies. For instance, histology or morphology is interpreted differently in SNOMED-CT and NCIT. While in SNOMED-CT, morphologies are specified as body structures, NCIT classifies them as diseases or disorders, reflecting a semantic divergence and ambiguity [44]. The task was further complicated by the insufficiency or absence of hierarchical (“is-a”) classification in several sources, such as OMOP Genomic, North American Association of Central Cancer Registries, Cancer Modifier, LOINC, and Unified Code for Units of Measure. These sources are commonly used to standardize entities such as laboratory or genetic texts, histological or pathological stages or grades, topography, and measurement units.

The heterogeneity and complexity revealed among the obtained mappings have necessitated the involvement of medical experts knowledgeable about biomedical ontologies to supervise the curation process. However, some conflicting representations and classifications remained unresolved because the exact meanings and specifications of concepts needed to be clarified and explicitly described in relation to foundational and core specifications. This can be achieved through high-level conceptual modeling practices.

The diversity and complexity of the application domain necessitated a design approach that enables a consistent yet simplified ontology-building process. In this regard, we applied ontology modularization [45], suggested in Neon [37] and SABiO [10], to facilitate the development process and support the ontology’s extensibility and reusability. However, we adopted a contextualized approach to organize the ontology into generic modular components (Figure 2), with a semantic separation among domain knowledge, shared cross-domain entities, and integration-oriented modeling constructs. Domain-specific biomedical knowledge is captured in the clinical and biological and imaging modules, while the common module provides cross-domain entities and shared value sets (eg, demographic attributes, measurement units, and qualitative result values) reused across modules. These modules, created by conceptual thematic grouping, are domain-based ontology parts designed to accommodate the various types of metadata provided by AI4HI. They establish explicit boundaries among areas of knowledge, with each module focusing on a specific set of domain-related concepts.

The clinical and biological module encompasses aspects such as cancer types and subtypes, histology and morphology, tumor marker tests and results, treatment and medications, family history, surgical and therapeutic procedures, and staging and grading methods. The Imaging module covers the essentials of the imaging domain, focusing on cancer, such as the imaging modalities (eg, MR (Magnetic resonance), PET (Positron emission tomography), and CT (Computed tomography)), as well as procedures, assessments (eg, PI-RADS (Prostate Imaging Reporting and Data System), BI-RADS (Breast Imaging Reporting and Data System), and LI-RADS (Liver Imaging Reporting and Data System)), and observations. Finally, the Common module defines patient demographics (eg, age, sex, gender, and race) and a wide range of qualifiers, such as staging or grading values (eg, pM1a, pM1b, G1, G2, G3, 1, 2, and 3), absence or presence findings (eg, not assessed, not mutated, and present), unit of measure (eg, percent and milliliter), as well as laboratory test results (eg, positive, negative, 1+, and 2+). Numeric classes (eg, 1, 2, 3, 1+, and 2+) included in this module correspond to value sets required by clinical staging systems and laboratory test results. These values, represented in the ontology, originate from datasets produced by the AI4HI projects (see ORSD version 1.1 [40]). In addition, 2 auxiliary modules (generic and specific) were introduced to support semantic integration with EUCAIM infrastructure and external data models by representing general observational structures, temporal events, and dataset-level metadata.

Hierarchically, we divide the ontology structure into 4 layers (Figure 2), each with varying granularity, ranging from the most specific to the most generic: domain-specific, domain, core, and upper. We adopted these layers based on Guarino’s classification of ontologies, regarding their level of independence for a particular task or point of view [9]. The domain-specific layer (DSL) determines the granularity level of the hyperontology. The cancer-related knowledge (clinical, biological, and imaging) provided by AI4HI is enclosed in this layer. The domain layer is built from the DSL’s content, considering the hierarchical (is-a) mappings harvested during the knowledge acquisition phase. We developed DSL and domain layer using a bottom-up strategy. We also enriched them with the resulting syntactic mappings using the Simple Knowledge Organization System (SKOS) profile [46], which permits annotating concepts with their preferred labels (skos:prefLabel), alternative labels or synonyms (skos:altLabel), and definitions (skos:definition). The oncology core concepts and relationships are represented in the core layer through the application of the ontology unpacking process [38]. This involves an ontological analysis that reveals the oncology ontological conceptual model, thereby ensuring semantic interoperability in accordance with FAIR principles [3]. OntoUML [30,31], a high-level ontology-driven well-founded conceptual modeling language, is used to ontologically analyze and represent the essentials of oncology, based on the mCODE specifications [28,29]. The OntoUML metamodel aligns with the Unified Foundational Ontology (UFO) [39], in which the OntoUML modeling primitives (eg, category, kind, subkind, role, phase, mode, quality, event, and situation) correspond to the ontological distinctions and axiomatization of UFO. UFO, which makes a fundamental distinction between endurants (objects or tropes) and perdurants (events), is a foundational ontology grounded in philosophical theories and developed to support conceptual modeling in various domains, including business process and ontology-driven modeling [30,31]. Based on FHIR, widely adopted by health care establishments that are increasingly implementing FHIR standards [47], mCODE is defined as a “Domain of Knowledge” implementation guide intended to demonstrate how to represent clinical concepts in general, with the aim of increasing interoperability in oncology.

The ontological analysis and conceptual modeling of oncology’s essential entities and their interactions are performed using a top-down approach, helping uncover the domain basics and resolving ambiguity. Finally, with expert help, the generic categories are maintained in the upper layer, the top level of the hierarchy. Combining bottom-up and top-down in a middle-out fashion has effectively supported a coherent ontological model that links oncology’s conceptual analysis with specific application knowledge.

Following SABiO [10], ontology evaluation verifies that the ontology is correctly built to meet the initial specifications, and validation ensures that it fulfills its intended purpose. While the former focuses on the internal quality of the ontology model, such as clarity, competency, and consistency, the latter ensures external validation of reusability and applicability, including its ability to represent real-world situations. We thereby incorporated multiple evaluation methods into a multidimensional strategy, including ORSD verification, ontology revision with experts, the formalization of CQs using the SPARQL Protocol and Resource Description Framework Query Language (SPARQL) [48], and consistency checking using logic reasoners. We also validated the ontology’s applicability to real-world cancer scenarios from AI4HI and its extendibility using new datasets.

We present formal outcomes derived from the bottom-up strategy, integrated with core content, and managed using ontology management tools such as Protégé [51]. These outcomes provide an overview of key classes, object and data properties, and annotations. The hyperontology version 2.0 (December 2025) [40], represented using World Wide Web Consortium OWL 2 [50], defines more than 3900 classes, 9700 subclassOf relations, 130 equivalence definitions, 180 object properties, 60 data properties, 2200 exact match mappings to standard terminologies, and 730 query criteria.

The classes or concepts defined in the hyperontology are derived from AI4HI, standard concepts collected via semantic mappings to standards, core concepts from mCODE, and specific concepts required for semantic integration within EUCAIM. The hyperontology, as a controlled, structured vocabulary, standardizes these concepts by assigning each one a unique EUCAIM ID. These IDs serve as reference identifiers, enabling data interoperability among concepts represented with different uniform resource identifiers or codes across different terminologies. For instance, many identifiers are assigned for “Malignant neoplasm of breast,” including “254837009” in SNOMED and “C50” in ICD-10. Meanwhile, the hyperontology assigns a unique concept ID (CLIN1000060) to the clinical concept and establishes semantic mappings to different terminologies. This strategy helps unify heterogeneous data within EUCAIM, enabling consistent interpretation and exchange. Besides, EUCAIM IDs are meaningful identifiers that, using descriptive terms, reflect the semantic context of concepts across the different ontology modules. For instance, clinical and imaging concepts have EUCAIM IDs with “CLIN” and “IMG” prefixes, respectively. Although the concepts are separated into different modules, they are semantically interrelated through various relationships—for instance, MRI of breast (IMG1016224) is linked to Malignant neoplasm of breast (CLIN1000060) and Breast (CLIN1063727) via the “hasRelatedCondition” and “hasDirectProcedureSite” object properties, respectively. Additionally, the concepts are mapped using label-based exact-match similarity with various standard terminologies and vocabularies. For example, Malignant neoplasm of breast (CLIN1000060), defined from ICD-10 (C50), is mapped using skos:exactMatch to SNOMED-CT (254837009), ICD-O-3 (8000/3-C50.9), and NCIT (C4872). The matching process incorporates various synonyms and definitions from different sources. For instance, “Breast cancer” and “Malignant tumor of breast” are standard alternative labels of Malignant neoplasm of breast (Figure 6A and E).

In the context of EUCAIM, “standard sources” for various categories (eg, cancer types, body parts, histology or morphology, and laboratory tests) are specified by the experts. For instance, ICD-10 and ICD-O-3 are considered primary sources for cancer conditions (eg, Malignant neoplasm of breast and Malignant neoplasm of ovary) and body parts (eg, breast and ovary), respectively. However, these sources may lack explicit representation of certain essential or domain-specific concepts. For instance, cancer subtypes, such as “Malignant tumor of cecum,” “Carcinoma of breast,” “Primary malignant neoplasm of breast,” and “Primary malignant neoplasm of brain,” lack standard coding in ICD-10. This necessitates using alternative sources, such as SNOMED-CT, ICD-O-3, and NCIT, to fill the gap and ensure comprehensive representation across different levels of granularity. Similarly, multiple sources are defined for tumor marker tests, which are primarily determined from LOINC (following the mCODE specification) and expanded using OMOP Genomic.

To handle the heterogeneity of sources, accurate (taxonomic) semantic integration across different terminologies is required. On the syntactic level, domain-specific concepts are also mapped to OMOP and FHIR by establishing an alignment that covers the OMOP domains and IDs, as well as FHIR resources.

Figure 5 illustrates a portion of the hyperontology surrounding the concept Malignant neoplasm of breast, focusing on (A) the structure of classes; (B) semantic patterns, such as equivalence, which define cancer conditions in terms of associated morphology and body site; (C) restrictions, such as associating the cancer type with cancer staging method, finding site, and histological types; (D) concept use that shows additional restrictions associating the cancer types with medication, surgical and imaging procedures, genetic mutations, and tumor marker tests; (E) annotations and mappings, such as alternative labels and exact match; (F) taxonomy of breast topography; and (G) taxonomy of tumor morphology.

Using Description Logic abstract syntax [52], we represent the equivalence semantic pattern applied to Malignant neoplasm of breast (Figure 6B), as follows:

This pattern, adapted from SNOMED, formally provides the semantic meaning of breast cancer by explicitly associating the cancer type with the corresponding body part and morphology, using the object properties hasFindingsite and hasAssociatedMorphology, respectively.

Incorporating logical definitions is increasingly being adopted in ontology development to support semantic interoperability and data integration [53]. With the support of clinical experts, various logical definitions have been created to clarify the semantic meaning of specific classes. These definitions, which act as simple design patterns, are used to explicitly and precisely define complex, ambiguous, or nonstandardized concepts in terms of other standard atomic concepts. By applying these definitions, logical consequences can be generated using a logic reasoner (eg, HermiT [54]), thereby inferring the position and classification of concepts within the ontology’s hierarchy [53].

For breast cancers sensitive to estrogen (ER) and progesterone (PR) receptors or biomarkers, tumor marker test results are often reported as hormone receptor status, classified as ER-positive (ER+), ER-negative (ER−), PR-positive (PR+), and PR-negative (PR−). These statuses have been interpreted and standardized in 2 ways (Table 1): (1) as conditions (disorders; eg, ER positive (SNOMED-CT, 416053008) and PR negative (SNOMED-CT, 441118006) tumor) or (2) tumor marker tests (eg, ER (LOINC, 85337‐4) and PR (LOINC, 85339‐0)) associated with their results (eg, Positive (SNOMED-CT, 10828004) or Negative (SNOMED-CT, 260385009)). To ensure data harmonization, a logical definition is created, informed by clinical expertise, to precisely define hormone receptor statuses as specific attributes related to breast cancer types and clarify their association with corresponding tumor marker tests and results. In Figure 6, the term “ER-positive” is defined logically. A formal description of this definition is given as follows:

While the object property Has_Associated_Tumor_Marker_Test connects cancer types with their associated tumor marker tests, hasAnswer links these tests with their corresponding qualitative results (eg, positive and negative). By creating this definition, we resolve data heterogeneity across data holders by harmonizing the interpretations of tumor marker test results.

Various subtypes of breast cancer have been defined by AI4HI (Table 2), including molecular subtypes (eg, Luminal A, Luminal B (HER2-negative or HER2-positive), HER2-enriched/HR-negative (non-luminal), and Triple Negative or Basal-like (all HR and HER2-negative)). These subtypes lack a clear semantic representation in standard terminologies. While Luminal A and Luminal B are defined in NCIT (under Breast Carcinoma by Gene Expression Profile (C53553)), specific types of Luminal B (HER2 negative or HER2 positive) and HER2-enriched do not have formal definitions in coding systems. Nevertheless, molecular subtypes can be interpreted and classified based on the presence or absence of specific hormone receptors (HRs) and proteins [55-57]: ER (Estrogen Receptor), PR (Progesterone receptor), HER2 (Human Epidermal growth factor Receptor 2), and Ki67 (Cell proliferation marker). Figure 7 illustrates the classification criteria for these subtypes, which have been validated by oncology and pathology experts.

For example, Luminal B-HER2 negative and HER2 positive (enriched)/HR negative (non-luminal) exhibit the following distinguishing features:

In the hyperontology, we explicitly defined these molecular subtypes by integrating diverse features. The logical definitions of Luminal B-HER2 negative and HER2 positive (enriched)/HR negative (non-luminal) are given as follows:

Consequently, breast cancer molecular subtypes are standardized, classified, and incorporated into the hierarchy (by inference) at precise levels of granularity.

Figure 8 illustrates 3 different logical definitions (Luminal B, Luminal B-HER2 negative, and HER2 positive (enriched)/HR negative (non-luminal)) and the inferred classifications of concepts (yellow background) using HermiT [54].

This section discusses the results achieved by applying the multidimensional evaluation and validation strategy. For consistency, we used the logic reasoner HermiT [54] (version 1.4.3), an OWL 2 inference engine, to check for logical errors and inconsistencies and verify the correct inference of implicit knowledge. For the requirement-driven evaluation, methods such as ORSD verification, ontology revision with expert help, and ontology testing by formalizing generic CQs using SPARQL were used. Finally, to validate the hyperontology’s applicability, reusability, and extensibility, ontology population with real-world scenarios from AI4HI and ontology extension with new datasets have been performed.

ORSD verification involves experts who did not participate in the ontology development process to ensure that the ontology meets the content or quality requirements documented in the ORSD. Specifically, the evaluation process focuses on the following steps:

This multistep, expert-driven approach ensures transparency, reproducibility, and a comprehensive assessment of how effectively the EUCAIM hyperontology meets its stated requirements.

Ontology revision is a collaborative process involving domain experts, such as oncologists, radiologists, and pathologists, who can help ensure that the hyperontology accurately reflects the domains of oncology and medical imaging. Throughout multiple meetings with the experts, we systematically revised and evaluated the ontology content, considering concept classification, semantic interactions, and consistency of standard terms. We asked for feedback on different questions, such as: (1) Are the generic categories semantically organized? (2) Are there cancer subtypes that should be included in the hierarchy? (3) Are the terms used in the ontology consistent from a clinical perspective? (4) Should any terms be redefined or harmonized with widely accepted standards? (5) Are the semantic relationships accurately represented? (6) Are any other relationships required?

Based on the experts’ feedback, we adjusted the classification system to include new classes or refine the hierarchy. For instance, cancer subtypes, such as cancer of the breast parts (eg, nipple and areola), have been included in the taxonomy under Malignant neoplasm of breast (ICD-10, C50). Additionally, classifying tumor marker tests has required expert intervention, especially for tests not included in the mCODE terminology binding [29], which covers tests defined in LOINC (eg, Estrogen receptor Ag [Presence] in Breast cancer specimen by Immune stain (85337‐4)). For instance, a specific verification was required for tumor marker tests for brain cancer (eg, IDH1, P53, ATRX, and MGMT), which are represented as genetic tests in OMOP Genomic. In contrast, mCODE distinguishes between tumor marker tests and genetic tests.

The experts have also helped expose erroneous assignments of concepts that were incorrectly mapped or standardized throughout the ETL process and provided as input for the ORSD. For instance, the term “breastfeeding,” which is required for assessing breast cancer risk factors, is mistakenly assigned to Breastfed (SNOMED-CT, 169741004), an infant feeding method, rather than to Breastfeeding (SNOMED-CT, 413712001), a breast function related to the mother. Another example is related to the degrees of relatives with cancer (First degree/Second degree), which are relevant for assessing genetic or familial cancer risk. These values or concepts are incorrectly mapped to severity degrees in SNOMED-CT (264500008/263868004) instead of blood relative degrees: First degree blood relative (SNOMED-CT, 125678001) and Second degree blood relative (SNOMED-CT, 699110007). These values are required to semantically define the family history findings (eg, Family history of malignant neoplasm of breast in first degree relative). Accordingly, the relevant concepts have been included in the ontology model.

To assess whether the hyperontology effectively covers the application domain and meets content or quality requirements, CQs have been formalized as SPARQL [48] queries that the ontology should answer. These queries help validate the hyperontology at the structure or schema (terminological box) level, focusing on ontology classes, relationships, hierarchies, restrictions, and axioms. Table 8 presents examples of SPARQL queries that translate some of the generic CQs previously described in this study. In addition to the prefixes used for SPARQL validation at the data level, owl [59] is required for schema-level validation.

To validate the applicability of the hyperontology, real-world breast and prostate cancer scenarios (in unstructured text) were collected from AI4HI to enrich and populate the hyperontology. We manually identified and extracted instances (individuals) and assigned them to the corresponding predefined classes or concepts. Semantic relationships are also maintained among the individuals, considering the object properties defined in the ontology model. The hyperontology is also used as a knowledge base, with its semantic content accessed via SPARQL queries. In what follows, we present 2 scenarios provided by ChAImeleon (breast cancer) and ProCAncer-I (prostate cancer).

In the breast cancer scenario, a female patient underwent various procedures, including mammography, ultrasound, and core needle biopsy of breast. These procedures informed multiple diagnostic outcomes, including tumor diagnosis of Ductal Carcinoma grade II, imaging assessments (eg, 5), clinical staging (eg, cT2 and cN0), and tumor marker test results (eg, ER-positive and PR-positive). Additionally, a hypofractionated stereotactic radiotherapy was administered, resulting in a complete response. In the prostate cancer scenario, the diagnosis of a male patient begins with prostate-specific antigen (PSA) laboratory tests and a digital rectal examination procedure. It progresses through imaging techniques, such as multiparametric MRI and fusion biopsy, followed by a prostatectomy surgical procedure. Diagnostic interpretations included imaging observations (eg, PI-RADS5), histological grading (eg, Gleason score 4+3), clinical staging (eg, cT2b and cN0), and pathological staging (eg, pT2 and pN0). Tumor maximum dimensions and volume were also considered in the diagnostic process (Textbox 2).

We manually mapped the various information to their corresponding semantic references (classes and properties), thereby populating the hyperontology with real-world data. This enabled the logic reasoner (HermiT) to infer the complete diagnosis, including the imaging findings and clinical or pathological interpretation results. Furthermore, we considered these results to query the populated ontology for cancer diagnosis information, thereby demonstrating its applicability and effectiveness as a knowledge base for extracting semantic content. Table 9 presents 3 queries to validate the hyperontology at the individual- or data-level (assertional box). We defined them considering query criteria specified in EUCAIM (eg, sex, age, cancer type, body part, staging/grading, tumor marker test or result, and clinical or imaging procedure). The queries are implemented using SPARQL [48], Owlready2 (a Python library for managing Ontology Web Language ontologies) [60], and Apache Jena Fuseki (a SPARQL server) [61]. The following prefixes are required for the query execution: rdf [62], rdfs [63], and eucaim [64]. Figure 9 illustrates the query results obtained by running a local Fuseki server.

The enrichment process aims to validate the extendibility of the hyperontology. Additional cancer types (n=17) have been incorporated by leveraging the contributions of new data providers (n=12) who have joined the EUCAIM platform. Examples of newly provided cancer types are thyroid, brain, ovarian, bladder, gallbladder, stomach, bone, and pancreatic cancers. The contributions integrate various types of clinical and imaging information, including cancer types, histological subtypes, laboratory or tumor marker tests and results, cancer staging or scoring, surgical or imaging procedures, and imaging modalities. Moreover, additional information is incorporated for cancer types already included in the hyperontology. For example, in the case of prostate cancer, PI-RADS is specified in the hyperontology as the standard method for interpreting prostate magnetic resonance imaging (MRI). However, data holders have also provided alternative assessment methods, such as the Likert scoring system [65], which reflect the radiologist’s expert interpretation. In addition, for prostate cancer, additional clinical procedures have been considered, such as Systematic Prostate Biopsy and Targeted Prostate Biopsy, defined in NCIT, enhancing the scope of removal procedures. For imaging, new modalities and techniques, such as Whole Slide Imaging (WSI) and Digitized hematoxylin-eosin (HE) slides, have been introduced, expanding the scope to encompass digital pathology imaging in conjunction with radiology techniques (eg, MRI and CT).

Regarding data formatting, the contributions are presented as sets of labels or values associated with variables defined within each data provider’s dataset. These labels, which are not standardized, exhibit some ambiguity or incompleteness, making data analysis and, thereby, the enrichment process challenging. In this regard, a structured process is required to ensure the efficient semantic integration and harmonization of information. Three main phases have been conducted throughout the enrichment process: (1) data analysis and extraction, (2) semantic mapping, and (3) ontology integration.

Table 9 presents an example of a new cancer type, thyroid cancer, with some clinical or biological and imaging information. Except for the cancer staging values (eg, N0 and N1a) and imaging modality (eg, scintigraphy), which are defined in previous versions of the hyperontology, the provided clinical and biological data are considered for enrichment and integration. Due to incomplete information, mapping to standards and terminologies is challenging and requires manual review. For instance, histological subtypes, such as “papillary” and “follicular,” can be mapped to various concepts from the OMOP Observation domain. The former is mappable to “Papillary carcinoma, NOS” (ICDO3, 8050/3), “Papillary adenocarcinoma, NOS” (ICDO3, 8260/3), and “Papillary thyroid carcinoma” (SNOMED-CT, 1336196002). Meanwhile, the latter maps to “Follicular carcinoma, NOS” (ICDO3, 8330/3) and “Follicular adenocarcinoma” (SNOMED-CT, 5257006). In this regard, the involvement of experts is required to clarify the terms, preventing any loss of granularity or precision during the mapping process. Figure 10 illustrates part of the ontology integration phase for thyroid cancer concepts (blue background), including cancer and histological subtypes, as well as body parts.

New standard concepts, such as Malignant neoplasm of thyroid gland, Thyroid, and Insular carcinoma (mapped to Poorly differentiated thyroid carcinoma; Table 10), have been integrated into the taxonomy and interconnected using semantic relations already defined in the ontology model (eg, hasFindingsite and hasAssociatedMorphology). Meanwhile, some additional taxonomic mappings have been conducted and incorporated into the hierarchy to enhance the semantic classification of terms. For instance, Malignant neoplasm of thyroid gland, classified as Malignant neoplasm of neck, is also a specification of Neoplasm of thyroid gland in SNOMED, introducing a new branch of concepts into the hyperontology’s taxonomy.

Table 11 presents some metrics for new concepts and relationships compared to those for reused ones. Extending and enriching the hyperontology, primarily at the domain-specific level, has broadened its scope, making it more applicable and valuable for querying, annotation, and segmentation applications and supporting the ETL process. Besides, the extension helped assess the coverage and pertinence of the core ontological level to which new concepts have been mapped and integrated. The enrichment process has demonstrated a solid foundation for hyperontology, particularly in the core and upper layers, which remained intact, and the efficient reuse of semantic relations.