The Cancer Data Center of the National Cancer Center Korea (NCCK), South Korea’s National Cancer Data Center, establishes the data infrastructure and utilization environment required to promote nationwide cancer data research. As part of these efforts, a clinical term-to-standard terminology mapping project was initiated. A multidisciplinary working group of 3 data experts, 7 health information managers, and 2 clinical oncologists was convened. Data experts, who are responsible for building and maintaining the National Cancer Data Center research database and NCCK Clinical Research Data Warehouse (CRDW), conducted source term extraction, integration of the mapping outputs into the database, and quality control to ensure data integrity and operational suitability for standardization and reuse. Five health information managers, with expertise in electronic medical record (EMR) data management, multicenter data standardization, and standard terminology mapping, served as primary mappers. Two senior health information managers (the Chief of Medical Information Management Team and the Terminology Governance Lead of the EMR Steering Committee) supervised the overall mapping process and performed final review and approval of all mapping outputs. The 2 dedicated clinical oncologists, 1 medical oncologist and 1 cancer surgeon, contributed domain expertise in cancer care to review whether the clinical meaning of the source terms was preserved during mapping. When specialized clinical interpretation was required, additional consultation was obtained from relevant physicians within the institution. The working group held regular monthly meetings and biweekly written discussions for project design, progress review, mapping issue discussion, and consensus resolution (Figure 1).

Candidate source terms were identified from clinical data in the CRDW, which collects comprehensive information from the EMR system and houses deidentified data for research. Patients diagnosed with one of 6 primary cancers (stomach, breast, liver, colorectal, pancreatic, or lung) at the NCCK between 2010 and 2023 were included. Inclusion was based on ICD-O-3 codes: C16 (stomach), C50 (breast), C18/C19/C20 (colorectal), C22 (liver), C25 (pancreas), and C33/C34 (lung). Patients with intrahepatic bile duct cancer (C22.1) and islet cell tumors of the pancreas (C25.4) were excluded. Clinical data were extracted and classified into 7 domains: chief complaint, diagnosis, laboratory test, imaging test, surgery/procedure, medication, and radiation therapy. The source terms underwent minimal refinement to preserve their original clinical meaning: correction of typographical errors and spacing, expansion of abbreviations, removal of duplicates, and splitting of multivalued terms. Electronic data interchange (EDI) reimbursement codes for Korean National Health Insurance (NHI) claims and their effective dates were extracted from the CRDW for reference during mapping.

Each source term was mapped to relevant standardized terminology systems according to its clinical domain. Domain-specific mappings included the Korean Standard Classification of Diseases (KCD), LOINC, International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) Volume 3, RxNorm, and the Anatomical Therapeutic Chemical (ATC); all terms, regardless of domain, were mapped to SNOMED CT (Figure 2). Although newer procedure classifications such as ICD-10-PCS have been developed, ICD-9-CM Volume 3 has been used by many countries and remains in use across several Asian countries because of its established role within national coding infrastructures and the operational complexity of migration [10-14]. In South Korea, ICD-9-CM continues to serve as the procedure classification reference for national health statistics, and clinical terms are generated and managed on this basis [15,16]. Therefore, ICD-9-CM Volume 3 was selected for the surgery/procedure domain to reflect the source data context.

All mappings followed the commonly recognized rules for each terminology system. For SNOMED CT, mappings adhered to the SNOMED CT Editorial Guide, compositional grammar, and concept model principles [17,18]. For postcoordination, focus concepts, attributes, and attribute values were selected in accordance with the permitted attributes and value ranges defined in the Machine Readable Concept Model (MRCM) [19]. The working group established and shared predefined guidelines with all mappers to specify mapping priorities and ensure consistency when multiple MRCM-compliant mapping options were available within the SNOMED CT framework. SNOMED CT mapping was categorized as “exact” when the target concept matched the source term in meaning, “broad” when the target concept was broader than the source, “narrow” when the target concept was narrower than the source, and “not-mapped” when no suitable target existed. Given the potential loss of semantic specificity and reduced fidelity to the original clinical meaning, narrow mappings were not counted as successful mappings, and grouped with “not-mapped” category as “narrow/not-mapped” in the final mapping performance analysis. Information that the SNOMED CT Editorial Guide identifies as outside the scope of the SNOMED CT International Edition—such as brand names in the medicinal product [20] and administrative or reimbursement-related procedure descriptions, including procedure counts or procedure order [21,22]—was not classified as a broad mapping when absent from the target concept. In contrast, the clinical meaning of the source term itself was considered essential to the mapping and had to be represented in the target concept for the mapping to be classified as exact.

Following initial mapping by 5 mappers each assigned to a designated subset of source terms, 2 peer mappers cross-reviewed each other’s mapping results and recorded agreement or disagreement. Any disagreement was escalated to the multidisciplinary working group for consensus resolution. Clinical oncologists verified that the clinical meaning of each source term was accurately preserved, while data experts and supervising health information managers assessed whether the mapping was operationally appropriate and not overly specific in a way that would compromise standardization and reuse. Final decisions were made by prioritizing (1) accurate preservation of the original clinical meaning and (2) consistent application of predefined mapping principles. Cross-verification results were summarized using the cross-verification discordance rate, defined as the proportion of source terms for which disagreement was identified during cross-verification.

This study analyzed a terminology dataset of clinical source terms extracted from the institutional deidentified data warehouse and their mapping results, rather than individual-level clinical records. The unit of analysis was the terminology concept rather than the individual patient, and the analytic dataset contained no identifiable personal information or patient-level linkage. Accordingly, this study was granted exemption by the Institutional Review Board of NCCK (No. 2026‐0204), with a waiver of informed consent. The study was conducted in accordance with the principles of the Declaration of Helsinki.

Among the 468,193 patients registered in the CRDW by 2023, 66,380 who were diagnosed with primary stomach, breast, liver, colorectal, pancreatic, or lung cancer between 2010 and 2023 were included. Following extraction, source terms underwent a refinement process, including typographical correction, expansion of abbreviations, duplicate removal, and splitting of multivalued terms (Supplementary Material S1 in Multimedia Appendix 1). No semantic modification or simplification was allowed. A total of 3472 clinical terms were finalized as source terms (Supplementary Material S2 in Multimedia Appendix 1). For the chief complaint domain, the 200 most frequent terms were extracted to prioritize the terms most relevant to routine clinical use. For the medication domain, information on drugs related to cancer prevention and treatment was extracted. All available clinical terms were extracted for the remaining domains.

While all mappings followed the official guidance of each terminology system, the working group established predefined guidelines incorporating oncology-specific considerations to better preserve the original clinical context of source terms and improve consistency across mappers when multiple clinically plausible and terminology-compliant mapping options were available. The predefined guidelines were as follows.

To map chief complaint and diagnosis terms to KCD, if the source term is semantically identical to a subterm of the KCD target term, it should be mapped to that subterm; otherwise, it should be mapped to the main term, which is the primary identifier for the KCD code. In the surgery/procedure domain, if a single-source term contains multiple surgeries or procedures, each surgery or procedure should be mapped individually. The Korean Standard Terminology of Medicine can be used as a supplementary reference for mapping to the ICD-9-CM [23]. Although the EDI codes for NHI claims may be considered when available, priority should be given to the meaning of the source term from the CRDW.

For SNOMED CT, each source term should typically be mapped to the relevant hierarchy; however, if a chief complaint indicates the purpose of admission (eg, “for endoscopic variceal ligation”), it should be mapped to the procedure hierarchy. In the medication domain, information outside the scope of SNOMED CT, such as brand name or clinical indication (eg, transcatheter arterial chemoembolization), should be excluded. When the focus concept lacks dose form or strength information, postcoordination should be used. In the radiation therapy domain, if the meaning of a source term is ambiguous, it should be clarified by referencing the EDI codes for NHI claims before mapping. Narrow mappings that might distort the original meaning of the source term should be avoided, and exact mappings should be prioritized over broad mappings. When multiple postcoordinated representations are possible within SNOMED CT Editorial Guide and MRCM-defined attribute constraints, focus concepts, attributes, and attribute values should be selected according to the priority rules summarized in Table 1.

All mappings followed the SNOMED CT Editorial Guide and MRCM. The priorities listed below were applied additionally, only when multiple valid mapping options existed within these rules.

SNOMED CT concepts are displayed in the format “concept identifier |term|.” The numeric identifier represents the SNOMED CT concept ID, and the term enclosed by vertical bars (“| |”) is the human-readable SNOMED CT term [18]. Numbered items (①, ②, ③, etc) indicate the priority order used in this study for selecting focus concepts or postcoordination attributes when a source term contained multiple mappable elements.

Table 2 provides examples of how source terms were mapped to the corresponding target terminology systems in each clinical domain and how the SNOMED CT postcoordination priorities described in Table 1 were applied. Supplementary Material S3 in Multimedia Appendix 1 provides further details on the selection of SNOMED CT postcoordination attributes. For instance, in the case of “Carcinosarcoma, connective and soft tissue, unspecified” in the diagnosis domain, no single target term representing both “① behavior + morphology + site” and “② morphology + site” could be identified. Therefore, the focus concepts “448274000 |Malignant neoplasm of connective tissue (disorder)|” and “269469005 |Malignant neoplasm of soft tissue (disorder)|” were used to capture the “③ behavior (malignant) and the sites (connective and soft tissue)” in a 1:N relationship. The remaining information, morphology, was captured using a postcoordinated expression: “116676008 |Associated morphology(attribute)| = 63264007 |Carcinosarcoma(morphologic abnormality)|.” In the laboratory test domain, following the priority order of “① Test name + sample type,” no single concept capturing both elements was available for “Anti-HCV, immunoassay.” The second priority, test name, was mapped to the focus concept “64411004 | Hepatitis C antibody measurement (procedure)|.” The remaining details were represented through postcoordination by specifying the “② (Detailed) sample type (specimen),” and “③ Test method (procedure)” as follows: “116686009 |Has specimen(attribute)| = 119364003 |Serum specimen (specimen)|” and “370129005 |Measurement method (attribute)| = 104417008 |Immunoassay, qualitative, multiple step method (procedure)|,” respectively.

After mapping all the source terms, we examined how each term was represented across multiple target terminology systems to identify system-specific characteristics (Table 3). In the chief complaint and diagnosis domains, KCD could not fully represent certain terms, such as purposes of admission (eg, “for transplantation work-up”) or morphological detail in “adenocarcinoma of sigmoid colon,” whereas SNOMED CT captured these terms via postcoordination. In the surgery/procedure domain, behavioral characteristics (eg, malignancy) could not be included in ICD-9-CM procedure codes, whereas SNOMED CT allowed behavior to be represented as attributes in addition to the procedure focus. In the laboratory test domain, LOINC supports the specification of the unit of measure (eg, mg/dL for phosphate) through recommended units, whereas SNOMED CT lacks this level of unit specificity. Conversely, LOINC could not capture additional contextual information, such as “(em)” denoting an emergency procedure, whereas SNOMED CT could via postcoordination. In the medication domain, RxNorm generally represented detailed medication information most completely; for example, it captured ingredient, dose form, strength, and brand name for “Avastin 400 mg injection (bevacizumab).” In contrast, SNOMED CT represented ingredient, dose form, and strength but typically did not include brand name and often expressed strength per unit (eg, 25 mg per 1 mL) rather than total dose. The ATC classification included only ingredient-level information. Although RxNorm was usually the most complete, exceptions existed; for example, for “Mitomycin-C Kyowa,” SNOMED CT provided more complete information than RxNorm.

The initial mapping performed by the assigned mapper was additionally reviewed by 2 peer mappers, who recorded their agreement or disagreement with each mapping result. A mapping was considered discordant if either reviewer disagreed with the initial mapping result, and such cases were referred to the multidisciplinary working group for consensus resolution. There were no major discrepancies in the chief complaint, imaging test, or medication domains; the remaining domains showed cross-verification discordance rates of 0.3% to 1.3%. In the diagnosis domain, the cross-verification discordance rate was 0.3%, primarily involving source terms containing more than 1 anatomical site. This was resolved by adding a rule specifying that if multiple sites in a source term belong to the same parent concept, the term should be mapped to that parent concept rather than mapped 1:N to each child concept. For example, “Neoplasm of uncertain or unknown behavior of liver, gallbladder, and bile ducts” should be mapped to “liver and/or biliary passages,” not separately to “liver,” “gallbladder,” and “bile duct.” The surgery/procedure domain showed a 1.0% cross-verification discordance rate, largely from inconsistent handling of parenthetical details; this was addressed by mapping to target terms that incorporate the parenthetical meaning whenever possible (eg, retaining “malignant” in targets for “excision of retroperitoneal tumor (malignant)”). A 1.3% cross-verification discordance rate in the radiation therapy domain was mostly attributable to limited familiarity with the SNOMED CT relationship groups. Even when the meaning of a source term is not entirely reflected in the fully specified name of a focus concept, relationship group attributes may convey the necessary details, negating additional postcoordination. For example, “external irradiation–high energy radiotherapy” should map to the SNOMED CT concept “high energy beam therapy,” which includes a device attribute in its relationship group that represents external beam delivery, eliminating the need to encode “external irradiation” and “high energy.” Through this process of cross-verification and consensus discussion within the multidisciplinary working group, final mappings were confirmed; as examples, the mapping results for the 20% most frequent terms per each clinical domain are provided in Multimedia Appendix 2.

Overall mapping rates were high across most domain-system pairs, with the chief complaint domain showing the lowest rates at 97.7% for KCD and 98.3% for SNOMED CT (Supplementary Material S5 in Multimedia Appendix 1). The narrow/not-mapped terms in the chief complaint domain were primarily attributable to ambiguity or insufficient specificity in the source terms, such as “oozing,” “prolapse,” “malfunction,” and “for admission,” for which a semantically appropriate SNOMED CT target could not be reliably selected. In the diagnosis domain, KCD achieved a 100% mapping rate, whereas SNOMED CT achieved 99.9% because a small number of source terms that could only be narrowly mapped (eg, malignant neoplasm of overlapping lesion of other and ill-defined sites) were treated as narrow/not mapped. Outside these two domains, all source terms were successfully mapped to SNOMED CT. In the medication domain, 1.2% of the terms were not mapped to RxNorm owing to the absence of codes for drugs approved only in specific countries (eg, TS-1). All source terms were successfully mapped to other systems (LOINC, ICD-9-CM Volume 3, and ATC) within their respective domains.

The detailed SNOMED CT mapping results are presented in Table 4. Among the exact mappings, precoordination predominated in chief complaint (64.7%), diagnosis (79.1%), imaging test (58.9%), surgery/procedure (63.1%), medication (90.1%), and radiation therapy (70.7%), whereas postcoordination was more frequent in laboratory tests (80.2%). In laboratory tests, postcoordination captures additional information such as quantitative/qualitative details and test methods beyond the focus concept. Broad mappings occurred most frequently in the imaging test (43.3%) and radiation therapy domains (40.7%). In the imaging test domain, broad mappings arose mainly because information on view and projection (eg, pelvic inlet and outlet), patient position (eg, decubitus, supine, and erect), and other acquisition techniques (eg, 3D reconstruction and low-dose protocols) could not be represented in the target concepts. In the radiation therapy domain, source terms in the treatment prescription subdomain showed a high broad mapping rate (61.3%), while source terms in the treatment site subdomain achieved a 100% exact mapping rate. Specifically, most broad mappings resulted from incomplete representation of the number of ports (eg, 3 ports) and field arrangement (eg, parallel-opposed 2 fields), whereas information on the preparation processes (eg, simulation and treatment planning), modalities (eg, tomotherapy), and radiation types (eg, proton therapy) was precisely represented in the target concepts.