Terminology binding of cancer pathology reporting protocols is specific to each particular malignant neoplasm type as defined by the publishing entity, such as the College of American Pathologists or the International Collaboration on Cancer Reporting.

Association of SNOMED CT concept with published data elements entails understanding the protocol content, the terminology definitions, and any conditional logic based on "nesting" of questions, that is, the necessary observable entity/observation data to record.

Optimally, the publishing bodies of the reporting protocols will incorporate and distribute SNOMED CT - Data element bindings as part of their documentation or software functionality. Given their domain expertise and the stakeholders they represent, these organizations are well-positioned to be qualified stewards of content and domain-specific distribution of encoded reporting protocols.

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Overall Principles

Performing SNOMED CT terminology binding for cancer synoptic reports involves the structured association of SNOMED CT concepts to precisely represent the meaning conveyed by the individual questions and each possible answer to these questions. This process aims to link observable entities to describe questions or attributes and morphologic abnormalities as answers, providing a standardized framework for recording detailed pathological observations. The diagram below illustrates the overall approaches to binding SNOMED CT to the questions and answers of cancer synoptic reports and distinguishes the binding principles for model meaning bindings from the principles for value set binding.

Model Meaning Binding

Model meaning binding is focused on connecting the meaning or semantics of SNOMED CT concepts to the data model used within a particular system or healthcare application. It involves aligning the clinical concepts from SNOMED CT with the structural elements of a specific data model or information representation framework. This ensures that the SNOMED CT concepts are integrated effectively and consistently within the context of the application's data structure, allowing for accurate data capture, storage, and exchange.

Model meaning binding is crucial for the seamless integration of SNOMED CT concepts into specific healthcare system structures, allowing for accurate comparison of models representing similar types of questions or attributes. It aligns SNOMED CT's meaning with system elements, enabling:

1. Interoperability: Facilitating accurate data exchange between different systems.

2. Standardization: Promoting consistent interpretation and use of clinical terminologies.

3. Accuracy: Allowing precise capture and interpretation of clinical information.

4. Efficiency: Streamlining SNOMED CT implementation for smoother healthcare processes.

By enabling the comparison of models representing the same type of questions, model meaning binding ensures harmonization and alignment between SNOMED CT concepts and the data model used, enhancing data consistency and healthcare quality across systems.

Observable entities are utilized to represent the "question" within the context of clinical observations in a structured manner. In the field of cancer synoptic reporting, these observable entities act as descriptors or inquiries about specific aspects or attributes related to a patient's condition or findings. For instance, an observable entity might describe the histologic type of a malignant neoplasm of a particular organ system.

Observable entities serve as the broader category or question, asking about a particular aspect of the pathology or clinical findings, while the morphologic abnormalities act as the detailed answers, providing specific information or characteristics observed within that category. For instance, the observable entity "Histologic type of malignant neoplasm of organ X" could be paired with various morphologic abnormalities to describe the specific type or characteristics of the tumor observed within that organ system. This approach enables a more structured and standardized way of recording and representing clinical observations and findings in the context of cancer pathology or synoptic reporting.

Value set binding refers to the process of associating or linking specific codes or concepts from SNOMED CT to a predefined list or set of codes. These sets are often tailored to fulfill a particular purpose within a system or application. Value sets define subsets of SNOMED CT concepts that are pertinent to a specific use case or scenario. For instance, a value set might be created to represent all concepts related to allergies or a specific clinical procedure.

SNOMED CT morphological abnormalities serve as valuable representations for a variety of essential data items in a cancer synoptic report. Additionally, concepts from the clinical findings hierarchy and the qualifier value hierarchy within SNOMED CT are employed to address specific answer options within the reports. Details and examples of bindings are found in the next section.

As outlined in the "Concept Areas Modeled" section, morphologic abnormalities are used to represent the actual observed abnormalities or characteristics identified during clinical examinations or pathological studies. In the context of cancer pathology, various morphologic abnormalities encompass different characteristics of tumor cells or tissues, such as adenocarcinoma, mucinous adenocarcinoma, or serrated carcinoma, each representing distinct pathological findings or characteristics observed within a specific organ system.

Colorectal Cancer Resection Reporting Protocol

Below is a section of the International Collaboration on Cancer Reporting (ICCR) Colorectal Cancer Resection reporting protocol. This page exemplifies some of the bindings for this data set, namely the data elements for 'histological tumor type' and 'lymph node status'. Similar binding exists for each question/answer set within the protocol. View an excerpt from the ICCR Colorectal Cancer Histopathology Reporting Protocol:

Binding of Histological Tumor Type

The diagram below shows the question and options for the 'histological tumor type' data element. The Observable entity to be measured/assessed is "Histological tumour type", which is represented by the SNOMED CT concept 1284862009 |Histologic type of primary malignant neoplasm of cecum and/or colon and/or rectum (observable entity)| and represents the "question" being answered. The possible "answers" or observations are listed in the table below with associated SNOMED CT concept bindings for each histology option.

Data Item: HISTOLOGICAL TUMOR TYPE

• Question

  • Type: Question

  • Cardinality: 1..1

  • Display: Histological tumor type

  • Binding: 1284862009 |Histologic type of primary malignant neoplasm of cecum and/or colon and/or rectum (observable entity)|

• Response options (Group)

  • Cardinality: 0..1

  • Options:

The diagram below shows the question and options for the 'lymph node status' data element. The Observable entity to be measured/assessed is "Lymph node status", which is represented by the SNOMED CT concept and represents the "question" being answered. The possible "answers" or observations are listed in the table below with associated SNOMED CT concept bindings for each option.

Question 1: Lymph node status

• Type: Question

• Cardinality: 1..1

• Display: Lymph node status

Question 2: Number of lymph nodes examined

• Type: Question

• Cardinality: 1..1

• Display: Number of lymph nodes examined

Question 3: Number of involved lymph nodes examined

• Type: Question

• Cardinality: 1..1

• Display: Number of involved lymph nodes examined

A key feature often needed in Synoptic Cancer reports and other structured data entry forms is the ability to activate or deactivate certain fields based on selections made in different fields.

This condition occurs when user interface designers create data input forms to improve data entry efficiency. Specifically, entry form logic often solicits a response for a high-level observation, such as the presence of neoplasm invasion to any lymph or blood vessel. A negative observation negates the need for further elaboration. However, a positive answer may require the pathologist to indicate if the invasion is present in small lymph and/or blood vessels or larger blood vessels. Since the context of the observation, that is, "present" or "absent" is carried by the observable entity, the SNOMED CT concept for the observable entity/observation pair is different. Management of this scenario can also be managed using logical conditions. These conditions can be managed in forms logic or other rubrics.

For example:

This type of logical connection between elements in the information model goes beyond what can be achieved with basic terminology bindings alone, necessitating an extra layer of representation. Upcoming chapters will explore how standards like HL7 FHIR offer structures specifically designed for this purpose.