Incorporating cancer synoptic reporting into clinical practices offers a range of benefits, including improved communication, enhanced research capabilities, and ultimately, better patient outcomes. Below is a summary of the general clinical use cases.

Diagnosis and Staging

Pathologists need to accurately diagnose and stage a patient's cancer.

Pathologists use the synoptic reporting system to document key diagnostic information, including tumor type, grade, size, margins, lymph node involvement, and metastasis. This structured data aids in determining the appropriate treatment plan.

Treatment Planning

Oncologists and multidisciplinary teams require comprehensive information for treatment planning.

Synoptic reports provide detailed information about the patient's cancer, helping oncologists choose the most effective treatment options. This includes critical information like cancer characteristics, markers, or spread, which helps choose available therapies like surgery, chemotherapy, radiation therapy, targeted therapies, and immunotherapy.

Surgical Procedures

Surgeons need precise information for performing cancer surgeries.

The reporting system captures critical details about surgical procedures, such as the extent of invasion, organ and lymph node involvement, and risk factors for complications. Surgeons can refer to these reports to ensure consistent and accurate surgical approaches.

Pathologists need to communicate important pathological findings to oncologists and other specialists.

Synoptic reporting includes standardized language to describe histological features, biomarker expressions, and genetic mutations. This enables clear communication of diagnostic and prognostic information to guide treatment decisions.

Researchers require standardized data for cancer studies and clinical trials.

Synoptic reports provide a structured dataset that can be easily aggregated and analyzed for research purposes. This promotes data-driven insights into treatment outcomes, survival rates, and disease trends.

Healthcare providers need to monitor patients' progress over time.

Synoptic reporting allows consistent documentation of follow-up information, such as treatment responses, recurrence, metastasis, and long-term outcomes. This facilitates ongoing patient care and enables early intervention if issues arise.

Healthcare institutions aim to maintain high standards and achieve accreditation.

Synoptic reporting helps institutions adhere to standardized reporting guidelines, ensuring the quality and accuracy of cancer-related documentation. This can support accreditation processes and improve overall patient care.

Health information needs to be easily shared among different healthcare systems.

Synoptic reports follow standardized formats, making it easier to exchange data electronically between different healthcare providers, institutions, and electronic health record (EHR) systems.

Medical education and training programs require illustrative case studies.

Synoptic reports serve as valuable educational resources for medical students, residents, and other healthcare professionals to learn about real-world cancer cases and treatment strategies.

Patients seek comprehensive information about their cancer diagnosis and treatment.

Synoptic reports, presented in a patient-friendly format, can help patients understand their condition, treatment options, and prognosis, empowering them to make informed decisions about their care.

There is no specific FHIR Resource applicable for Synoptic Cancer Reporting, and the diversity of information required in different types of cancer makes it very difficult to use a single FHIR resource; this leads to the need for a specific FHIR resource for every kind of cancer.

Creating a new FHIR resource is a collaborative and iterative process. It requires significant engagement with the healthcare community to ensure that the resource is both technically sound and clinically relevant. The ultimate aim is to facilitate interoperability and improve patient care by representing health data in a standardized and meaningful way.

These are the typical steps involved in the creation of a new FHIR Resource:

1. Identification of Need :

The Cancer Synoptic Reporting Project Group followed a template-based, subject matter expert-driven process to develop the content produced in this project. Content developed is based on the content represented in published reporting protocols using the Observable entity concept model. The overall approach and assumptions are described in the following pages.

This chapter delves into two crucial aspects of cancer synoptic reporting with SNOMED CT: information models and terminology bindings.

Information models serve as the framework for organizing cancer data elements, guaranteeing consistency and interoperability across various systems.

Terminology bindings forge the essential connection between these data elements and SNOMED CT concepts, ensuring uniform representation and exchange.

The chapter provides practical insights into designing the general cancer report structure, including sample forms to demonstrate cancer synoptic reporting protocols.

Additionally, detailed explanations of SNOMED CT bindings are provided, highlighting their significance in seamlessly implementing standardized cancer data capture and exchange mechanisms.

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The cancer pathology report follows a general structure. The synoptic report is a summation of the required diagnostic and prognostic data elements identified in each of the following steps:

1. Case. A case is defined as a review of all tissue excised from the patient during a single surgery. (Note: In many surgeries, multiple organs or portions of organs are excised. Each excised organ, or portion of an organ, is considered a "Part".)

2. Gross Description. Each tissue part excised is visually described by the pathologist as received from the surgeon/surgical suite. Information documented includes what organ, or portion of an organ, the part consists of, the overall appearance, weight, and size. The Gross Description is a non-microscopic assessment of the tissue to be microscopically examined.

3. Microscopic examination. This section of the pathology report is performed and recorded after each part is prepared for microscopic examination. Preparation includes dissecting portions of each part; fixing the tissue in formalin, which stops tissue metabolism and degradation; embedding the tissue in paraffin wax; microtome (very, very thinly slicing portions) of the paraffin-embedded tissue; mounting microtome tissue onto glass slides; and staining the mounted tissue using prescribed diagnostic staining techniques, primarily hematoxylin and eosin.

Upon tissue preparation, tissue specimens are examined using light microscopy. The pathologist assesses each slide and determines the notable presence and absence of normal and diseased portions of the tissue. The report may consist of a textual review of each part or a summative enumeration of observations.

1. Additional studies. In this section, additional diagnostic and/or prognostic information is described. This may include review of immunohistochemically stained tissue, cytogenetic examinations, or gene sequencing results.

2. The synoptic report. One or more tissue parts examined are considered diagnostic and representative of the case in toto. If the case results in a diagnosis of cancer, a synoptic protocol is completed for the case. Usual practice is to associate the protocol with a single part as submitted, which is considered representative of the entire case, and supplement the protocol summation with notable components from other parts. For example, a colon resection will consist of portions of the colon and lymph nodes. Each portion of the colon and the lymph nodes are treated as different parts. Thus, a colon cancer diagnosis will be rendered and associated with a colon part, and the presence of lymphatic involvement will be based on the lymph node parts but included in the overall cancer report associated with the colon part.

Reporting protocols are specific to the primary organ system (anatomic location) of the malignant neoplasm. The specific aspects of the neoplasm to be assessed and the acceptable observations are enumerated within each protocol. Types of necessary observations follow common concept modeling, but terminology binding is unique to each protocol. Terminology bindings for College of American Pathologists (CAP) and International Collaboration on Cancer Reporting (ICCR)-based reporting protocols are available from each organization.

Historical SNOMED CT content authored for use in cancer synoptic reporting can be found in the Observable entity and Clinical finding hierarchies.

Observable Entities vs Clinical Findings

The vast majority of these concepts are primitive and have effective dates of 2001-01-31, which is the beginning of SNOMED CT time. These concepts were deemed insufficient to unambiguously represent pathology observations and findings for use in cancer registries. An early design decision in the Cancer Synoptic Reporting Project Group project was to use the Observable entity hierarchy instead of the Clinical finding hierarchy. Regardless of approach, substantial concept modeling would be required in either hierarchy. Ultimately, the decision was made to provide a tangible, needed, and practical use case upon which to demonstrate the efficacy of the newly remodeled Observable entity concept model. Apart from the novelty of the approach, the question, or Observable entity, in each synoptic report must carry sufficient context to unambiguously interpret the observation or finding.

A practical consideration for this modelling approach pertained to the amount of new content that would be necessary to create to meet the needs of the cancer synoptic use case.

Content Development Approach

A significant number of concepts have been created as part of this effort, encompassing all hierarchies. In retrospect, the decision to utilize observable entities to encompass the complete clinical context for each observable entity/observation pair proved beneficial in managing the concept volume effectively.

The histologic type of "malignant neoplasm of organ X" is a good example of this.

Every organ system with a reporting protocol has an average of 10-20 morphologic abnormalities that could be recorded. Many of these morphologies may be observed in multiple organ systems. If clinical findings were used to represent the protocol data, a new concept would be necessary for every organ/morphology pair for example:

• adenocarcinoma of organ X

• mucinous adenocarcinoma of organ X

• serrated carcinoma of organ X

Using observable entities, however, required only a single concept to be created for each organ system The observable entity (e.g., Histologic type of malignant neoplasm of organ X) could be paired with any number of valid morphologies. Ultimately, the decision required less new content to be developed and maintained.

It should be noted that the observable entity/observation pairs in the Cancer Synoptic Reporting Project Group product do NOT follow the clinical finding MRCM specific that uses the defining attributes = << AND = << . The types of information solicited in the cancer pathology protocols extend beyond the content represented in the Qualifier value hierarchy and include values from the Procedure hierarchy, Body structure hierarchy, and concepts in the qualifier hierarchy NOT subsumed by << and discrete numerical values. Current MRCM rules for clinical findings do not include these concept areas in the range of possible concepts for . Pathology synoptic data elements simply record a series of individual observations in a structured fashion.

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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.

This page highlights how cancer synoptic reporting contributes to accurate diagnosis, appropriate staging, tailored treatment planning, and ongoing patient management across various types of cancer. Keep in mind that this list isn't complete, but it offers common examples of specific clinical applications of cancer synoptic reporting.

Breast Cancer Lumpectomy

A patient with early-stage breast cancer undergoes a lumpectomy.

The synoptic report documents details, such as tumor size, margins, lymph node involvement, and any additional findings. This information helps oncologists determine if further treatments like radiation therapy or chemotherapy are necessary.

Colon Cancer Resection

A patient undergoes surgery to remove a tumor in the colon.

The synoptic report records the extent of the resection, involvement of adjacent structures, lymph node status, and whether the tumor breached the serosa. This aids in staging the cancer and planning subsequent treatments.

Prostate Cancer Biopsy

A patient undergoes a prostate biopsy due to elevated PSA levels.

The synoptic report captures the number of biopsy cores taken, the Gleason score (a measure of cancer aggressiveness), and the percentage of cancer involvement in each core. This information guides treatment decisions.

A patient is diagnosed with non-small cell lung cancer.

The synoptic report documents tumor size, lymph node involvement, and any distant metastases. This information helps stage the cancer using the TNM (Tumor, Node, Metastasis) system, informing treatment options.

A patient with ovarian cancer undergoes surgery to remove as much tumor tissue as possible.

The synoptic report records the extent of debulking achieved, the presence of residual disease, and the involvement of nearby organs. This information guides decisions regarding subsequent chemotherapy.

A patient has a suspicious melanoma lesion removed.

The synoptic report details the Breslow thickness (a measure of tumor depth), Clark level (depth of invasion), ulceration status, and mitotic rate. These factors contribute to determining prognosis and treatment strategies.

A patient undergoes surgery for gastric cancer.

The synoptic report documents tumor location, depth of invasion, involvement of adjacent structures, and lymph node metastases. This information guides treatment decisions, including surgery and chemotherapy.

A patient is evaluated for hematologic malignancy.

The synoptic report includes details about bone marrow cellularity, percentage of blasts, presence of chromosomal abnormalities, and any immunophenotypic findings. This aids in diagnosing and classifying the malignancy.

A patient undergoes a Pap smear for cervical cancer screening.

The synoptic report records the cytological findings, the presence of high-risk human papillomavirus (HPV), and any abnormal cellular changes. This information guides follow-up and management.

A patient with a brain tumor undergoes surgery for tumor resection.

The synoptic report documents tumor type, location, extent of resection, and involvement of critical structures. This information informs treatment decisions and post-operative care.

HL7’s Fast Healthcare Interoperability Resources (FHIR) has emerged as an industry standard for representing and exchanging electronic health data. When dealing with synoptic cancer reports, there are different ways to represent the information using FHIR resources. Two of the options include creating a new FHIR resource or using the FHIR Questionnaire resource. The decision between creating a new FHIR resource or using the FHIR Questionnaire largely depends on the specific needs of the project, the desired level of granularity, and the available resources for development and maintenance. A thorough analysis considering both the short-term implementation and long-term maintenance aspects will help guide the best approach.

New FHIR Resource for Synoptic Cancer Report

Pros

1. Tailored Representation: Creating a bespoke resource allows for a more tailored and granular representation of the specific data elements in synoptic cancer reports.

2. Improved Semantics: Custom semantics can be built into the new resource, ensuring that the meaning of the data is captured more precisely.

3. Standardized Structure: With a custom resource, all implementers would follow the same structured format, promoting consistency across systems.

4. Optimized Queries: Custom indices can be built into the resource, potentially optimizing query performance.

1. Development Time: Creating a new resource requires more time and effort, from design to validation to publication.

2. Adoption Barrier: Introducing a new resource might create an adoption barrier, as systems need to be updated to recognize and process this new entity.

3. Maintenance: There’s a need to maintain and update the new resource in line with FHIR’s evolution and updates in clinical knowledge.

1. Pre-existing Structure: Leveraging the FHIR Questionnaire means using an already defined and recognized resource, potentially speeding up development.

2. Flexibility: Questionnaires are inherently flexible and can be adapted to capture various kinds of data, including that of synoptic cancer reports.

3. Broad Adoption: Since the Questionnaire resource is already a part of the FHIR specification, many systems will already support it, potentially easing integration efforts.

1. Generalized Semantics: As a generic tool, Questionnaires might not capture the specific semantics of synoptic cancer reports as precisely as a dedicated resource.

2. Potential Complexity: Capturing complex clinical data in a questionnaire format can become unwieldy or confusing.

3. Less Optimized: Queries might be less efficient when searching for specific data elements in a generic Questionnaire compared to a custom resource.

The following sections will outline both methods. However, it's important to note that in most cases, the advantages of using FHIR Questionnaires outweigh the reasons for developing new FHIR Resources. The upcoming implementation examples will, therefore, be provided as FHIR Questionnaires.

Cancer reporting protocols are published by national and international societies of pathology. The US and Canada employ the College of American Pathologists versions. Many nations in Europe and Australasia use the International Collaboration of Cancer Reporting (ICCR) published protocols as the foundation of national cancer reporting data sets. The ICCR is a collaboration of several national and regional societies of pathology including the CAP, the Royal Colleges of Pathology (UK and Australasia), and the European Society of Pathology.

Below are links and examples for the College of American Pathology (CAP) and the International Collaboration for Cancer Reporting (ICCR)

• CAP reporting protocols can be freely accessed and viewed at https://www.cap.org/protocols-and-guidelines/cancer-reporting-tools/cancer-protocol-templates.

• ICCR protocols can be freely accessed and viewed at:

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

SNOMED CT

https://www.snomed.org/education

ICCR

https://www.iccr-cancer.org

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In the following section, we delve into two distinct use cases that vividly illustrate the multifaceted advantages of cancer synoptic reporting. These use cases not only underscore its pivotal role in guiding clinical decision-making but also highlight its invaluable contributions to research endeavors, quality assurance, and overall healthcare excellence.

Scenario 1: Colorectal Cancer Staging and Treatment Planning

Background:

A 58-year-old patient presents with symptoms indicative of colorectal cancer. Following diagnostic tests, it is confirmed that the patient has adenocarcinoma of the colon, and a treatment plan needs to be formulated.

Description:

• Synoptic Reporting : The oncology team utilizes a cancer synoptic reporting system to create a comprehensive report for the patient. The report captures crucial information about the tumor, including its size, location, histological type, grade, and lymph node involvement. The synoptic template prompts the clinicians to input standardized data, ensuring consistent and accurate documentation.

• Staging Accuracy : The structured synoptic report allows the oncologists to accurately stage the cancer using the TNM (Tumor, Node, Metastasis) system. The report includes details about the depth of tumor invasion, the number of affected lymph nodes, and the absence or presence of distant metastases. This precise staging information aids in determining the optimal treatment strategy.

• Treatment Plan : Based on the synoptic report, the oncology team can confidently recommend an appropriate treatment plan. In this case, the patient's cancer is determined to be at an early stage with no lymph node involvement. Therefore, the patient becomes a candidate for surgical resection. The synoptic report's standardized data helps the surgical team understand the extent of the surgery required and enables a focused approach.

• Post-Operative Follow-up : After surgery, the synoptic report continues to play a role. It documents the success of the resection, ensuring that clear margins were achieved. This information becomes a part of the patient's medical record, guiding future monitoring and potential interventions if necessary.

Through the use of cancer synoptic reporting, the patient's colorectal cancer is accurately staged, and a tailored treatment plan is initiated. The structured documentation contributes to informed decision-making, improves communication among healthcare professionals, and enhances the patient's overall care journey.

A 45-year-old patient undergoes a mastectomy due to an aggressive form of breast cancer. Pathologists are tasked with assessing the tumor's characteristics and providing accurate information for treatment planning and research purposes.

• Synoptic Reporting : Pathologists employ a synoptic reporting system to record detailed information about the tumor. The report covers factors such as tumor size, histological type, nuclear grade, lymphovascular invasion, hormone receptor status, and HER2/neu expression. This structured data ensures consistent reporting across cases.

• Treatment Guidance : The synoptic report's data is essential for guiding the patient's treatment plan. The receptor status information, including estrogen and progesterone receptors as well as HER2/neu expression, helps oncologists determine appropriate targeted therapies such as hormone therapy or HER2-targeted agents.

• Research Contribution : The structured synoptic data is not only confined to the individual patient's care. Aggregated and anonymized synoptic reports contribute to research initiatives. Researchers can analyze the data to identify trends, assess treatment outcomes, and develop insights into the effectiveness of different therapies across various subtypes of breast cancer.

Through the utilization of cancer synoptic reporting, the pathologists provide accurate diagnostic information to guide the patient's treatment plan. Additionally, the structured data contributes to ongoing research efforts, enhancing the collective understanding of breast cancer subtypes and treatment outcomes.

These detailed use cases underscore the tangible benefits of cancer synoptic reporting in enhancing clinical decision-making, enabling research, and maintaining high standards of quality in cancer care.

Creating a FHIR Questionnaire for synoptic cancer reporting provides a simple and expedited way of representing custom information models with the flexibility of a dynamic specification that can adapt quickly to any required change. Each cancer type will use a new questionnaire definition in this approach, with some sections in common and some sections with specific content. Open-source tooling is available for authoring questionnaire definitions, and the questionnaires can be easily rendered in a clinical application for supporting data capture.

The information entered in a FHIR questionnaire can be shared as a FHIR Questionnaires Response or transformed into a bundle of specific FHIR resources like Observations Resources and others.

Creating a new questionnaire requires several methodical steps. Here’s a step-by-step guide to help you accomplish that:

1. Define the Scope :

   • Understand the specific type of cancer and the information that needs to be captured in the synoptic report.

   • Determine the purpose of the Questionnaire: Is it for diagnostic purposes, treatment monitoring, research, etc.?

2. Gather Information :

   • Gather all the relevant clinical guidelines, standard synoptic templates, and any other resources that can guide the creation of the Questionnaire.

   • Consider input from oncologists, pathologists, and other stakeholders.

3. Design the Questionnaire :

   • Start by identifying the main sections or groups of the Questionnaire. For instance, patient information, tumor characteristics, treatment history, etc.

   • For each section, define the questions, possible answers, and any constraints.

4. Use FHIR Tools :

   • Utilize tools like the or any other FHIR-compatible tool to help design, visualize, and test your Questionnaire.

   • These tools can help ensure the Questionnaire is constructed correctly according to FHIR standards.

5. Incorporate Conditional Logic :

   • If certain questions should only appear based on the answers to previous questions, incorporate this conditional logic. For instance, if a specific treatment is selected, additional questions related to that treatment may be necessary.

6. Iterative Testing :

   • Test the Questionnaire in a FHIR-compatible system.

   • Collect feedback from potential users, like pathologists or oncologists.

7. Integrate with EHR Systems :

   • Ensure that the Questionnaire can be integrated into Electronic Health Record (EHR) systems or any other health IT system where it will be used.

   • Consider aspects like how the data will be extracted, how it will be presented to clinicians, and how it will be stored.

8. Training & Education:

   • Once the Questionnaire is ready, provide training to potential users.

   • Create educational materials, guidelines, and best practices for completing the Questionnaire.

9. Continuous Review & Updates:

   • As medical knowledge evolves and new guidelines emerge, the Questionnaire should be reviewed and updated accordingly.

   • Set up regular intervals (e.g., annually) to review and make necessary modifications.

10. Interoperability & Sharing:

    • Consider sharing the designed Questionnaire with the broader medical and FHIR community. This can aid in standardization and promote interoperability.

11. Documentation:

    • Ensure you document the design choices, versions, and updates of the Questionnaire. This documentation is crucial for maintaining and updating the Questionnaire in the future.

12. Compliance & Ethics:

    • Ensure that the Questionnaire meets legal, ethical, and regulatory standards, especially when dealing with sensitive health data.

Remember, the aim of creating a FHIR Questionnaire for synoptic cancer reporting is to capture standardized, structured, and clinically relevant information that can be easily shared, analyzed, and utilized for patient care. It’s crucial to keep end-users in mind throughout the process and prioritize clarity and ease of use.

Three approaches for cancer synoptic data recording are described in this section: paper-based forms, distributed electronic forms, and a centralized reporting platform. Each of these approaches has benefits and drawbacks, which are described below. In addition, the emerging Fast Healthcare Interoperability Resources (FHIR) model is an elegant hybrid of central registry reporting and electronic health system integration. Although FHIR implementations for cancer synoptic reporting are in pilot phases only, the approach is also described.

Paper-based Approach

Structured, synoptic cancer reporting can be realized using a paper-based system. Multiple organizations release paper-based forms for cancer synoptic reporting.

Pathologists can manually record their observations using these pro forma templates. This approach does provide structure and enhances the completeness of data records. However, it does not directly render cancer pathology data into computable form. That can only happen with a transcription or abstraction of the paper form into an electronic system that is encoded using SNOMED CT.

The limitations of this approach are readily apparent. Yet, in an environment where electronic health record systems are not readily available, this approach to pathology cancer reporting can be effective for completeness of reports for immediate use by clinical care teams, and these forms can be used by public health authorities to populate central cancer registries for surveillance and disease management efforts.

This approach requires that publishers of cancer pathology data sets render their protocols (pro forma templates) into a format that can be ingested and used by EHR and LIS software platforms. The EHR/LIS vendor software then use these electronic representations to create an electronic version of the paper form for the user to complete as part of their usual reporting workflow.

Benefits of this approach:

• Structured, encoded cancer pathology reporting is fully integrated into usual pathology documentation/reporting workflows

• Centralized management and distribution of curated content

• Ability to customize workflow within institutional EHR/LIS

Limitations of this approach:

• Relies on a standard interoperability framework

• The approach relies on software vendors to implement content in accordance with the publisher's intent

• The approach relies on software vendors to incorporate encoded pathology cancer data into EHR/LIS data models

Example

In the United States, the College of American Pathologists (CAP) developed a process to render their published reporting protocols into a XML documents that electronic health records (EHR) and Laboratory Information System (LIS) vendors. This approach was unique to the US and Canada for many years. It is now expanding into other parts of the world through middleware vendors that customize cancer pathology datasets for incorporation into EHR/LIS workflows. It is the longest-standing electronic method of capturing cancer pathology reports in a computable fashion at the time of report generation.

In this approach, pathologists interact with a centralized application rendering the specific reporting form. Upon completion of the form, the data is fully encoded and stored within the central cancer registry. A PDF or other electronic form of the report is sent back to that pathologist for incorporation into the patient's medical record.

Benefits of this approach:

• Central management of cancer pathology reporting data sets

• Immediate incorporation into the registry

• Sophisticated user interface logic is easily incorporated into the user interface to optimize workflow efficiency, ensuring only the required data elements are presented for pathologist recording.

Limitations of this approach:

• Requires "leaving" EHR/LIS to complete the report. Can be additional work for the pathologist

• Documentation/ entry of data is required in both the EHR and the central web portal

• External report may be returned only as a PDF or other non-computable form

Example

This approach is used in the Netherlands by PALGA and is emulated in other nations, which use a centralized web portal for cancer pathology reporting. Here, pathologists navigate to a web portal managed by national cancer registries.

Knowledge Representation Model

The underlying principle of the Cancer Synoptic Reporting Project Group is that the "question" (represented by a SNOMED CT observable entity) should include all the context needed to clearly understand the "answer" (or observation) that is recorded. The criteria for deciding these results depend on additional information and knowledge that can't be conveyed by SNOMED CT alone, such as clinical guidelines.

For example, the differentiation between an adenocarcinoma and a mucinous adenocarcinoma is based on the amount of mucin measured in the cells and the organ system involved. In breast tissue, the amount of mucin in the cells to be considered a mucinous adenocarcinoma is > 80% but in the colon is > 50%. The pathologist is responsible for this knowledge, not SNOMED CT. So, this type of explicit knowledge of the pathologist is outside of the scope of the SNOMED CT concepts and will not be discussed further in this document.

The observable entity/observation pairs below each state that the histologic type of the neoplasm assessed is a mucinous adenocarcinoma in the breast and in the colon. It is the SNOMED CT observable entity that provides the context for the observation, specifically the organ system of concern. It does not directly state the amount of mucin in the cells as observed by the pathologist. The pathologist exercised domain-specific knowledge to reach such a conclusion.

Mucinous adenocarcinoma of the breast: = reflects the pathologist's interpretation of the microscopically evaluated slides where the percent tumor cells containing mucin as a proportion of the total number of tumor cells is > 80%.

Mucinous adenocarcinoma of the colon: = __ reflects the pathologist's interpretation of the microscopically evaluated slides that the percent tumor cells containing mucin as a proportion of the total number of tumor cells is > 50%.

Therefore, it is important to understand that the observable entity/observation pairs used throughout the cancer pathology synoptic reporting use cases reflect point-in-time observations as ultimately assessed and interpreted by the pathologist. Domain knowledge specific to the practice of pathology and oncology is NOT intended to be represented by the terminology, but rather, the terminology represents what the observation was and is based on specific domain expertise.

As noted, the synoptic pathology report is comprised of a list of characteristics of the neoplasm that are required to be observed and reported by the pathologist. Each characteristic is modelled using the** 363787002 |Observable entity (observable entity)|** hierarchy and concept model.

Major categories of neoplasm characteristics required in each report are listed below.

• Procedure used to collect the specimen(s)

• Tumor site

• Tumor dimensions

• Histologic type

The list of possible observations that can be made for each characteristic is comprised of a constrained list of acceptable observations (i.e., value sets). For example, a list of acceptable histologic types (morphologic abnormalities) is provided to the pathologist to select when reporting the histologic type of the neoplasm. Semantic types of these value sets are dependent upon the |Property| target value.

Semantic types in a response value set are all of the same semantic type, with the exception of the use of |Qualifier value| concepts employed for pathologist observations, such as 385432009 |Not applicable (qualifier value)| or 1156316003 |Cannot be determined (qualifier value)| .

For example, the concept, , is modeled with = . The range of possible observations must be << .

In addition to the template constraints, the protocol publishers further constrain the acceptable value sets to include only those values that are possible for a particular malignant neoplasm. For example, the possible values for histologic types in the lung protocol would never contain values for Germ cell neoplasms (a condition only possible in reproductive organs).

The Cancer Synoptic Reporting Project Group operated under the data modeling paradigm that the |Observable entity (observable entity)| concept provides the context to correctly and unambiguously interpret the observation. Therefore, understanding the observable entity concept model is fundamental to use of the cancer synoptic content. Authored content for observable entities of this project are found <<1145211006 |Proliferative mass observable (observable entity)|

The table below provides an overview of the defining attributes used to author cancer synoptic reporting concepts, including a description of the target values for these attributes.

• << ;

• << ;

• < ;

| This attribute is used to assert the entity that carries the property being measured. In most cases, the target values are << | The inherent location attribute is used to describe the anatomical location of the entity that carries the property being assessed. In most cases, the inherent location indicates the anatomical location of the primary malignant neoplasm, that is the primary organ affected by the malignancy. | This attribute is used to indicate an entity that is being assessed for presence such as necrosis within a neoplasm. It is also used to represent the numerator in a percent observation. | This attributed is used for the denominator in a percent or number fraction observable. | Direct site is specifically used to define the specimen in which the observation is being made. | Technique is used to define the method by which the observation is being made. This attribute is used to specific methods of tumor staging, histologic grading methods, direct vision (gross) evaluation, microscopy and immunohistochemistry methods. | The time aspect for all cancer pathology observable entities is

| This attribute is used to describe the evaluation scale used for the observation.

• • is used to describe observable entities assessing morphologies, body structures, and procedures.

| Characterizes is used to represent the underlying processes of the neoplasm. These include << and | This attribute is used to define the "end point" of the process indicated by the characterized attribute/value pair. In most concepts, this associated value of this attribute is << to indicate where the neoplasm has grown or metastasized.

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.

Cancer synoptic reporting plays a pivotal role in delivering not only clinical advantages but also an array of benefits that extend to areas such as interoperability, surveillance, quality assurance, and research. This structured and standardized approach to documenting cancer-related information brings forth a harmonized approach that fosters seamless data exchange, enhances monitoring capabilities, ensures high-quality care standards, and contributes to the advancement of medical research.

In the subsequent pages, a range of general and clinical use cases will be outlined, and two detailed use cases will be presented to illustrate the use of cancer synoptic reports.

The SNOMED CT Clinical Implementation Guide for Cancer Synoptic Reporting is a practical resource to support standardized, structured reporting in cancer care. By using SNOMED CT clinical concepts to represent diagnoses, treatments, and outcomes, healthcare organizations can improve the quality of captured data, enable information exchange, and strengthen collaboration across care settings.

The guide is written for both clinicians—to understand the relevant SNOMED CT content - and for vendors and system developers - to support implementation in healthcare systems.

It highlights the role of synoptic reporting in improving cancer care by ensuring that key data elements are consistently documented and easily shared. Examples are provided across oncology use cases, including hematologic malignancies, cervical cancer screening, and neuro-oncology tumor resections.

Finally, the guide shows how SNOMED CT and existing standards such as HL7 FHIR Questionnaires can be combined to create interoperable synoptic reporting forms. This approach promotes accuracy, reduces variability, and supports research, quality improvement, and patient outcomes.

Contents

In this chapter, the considerations related to the technical application of cancer synoptic reporting forms with SNOMED CT are presented. It explores the emerging use of FHIR for standardized model representation, comparing creating new FHIR resources to using FHIR Questionnaires. Benefits and limitations of each approach are outlined, with a focus on the practicality and advantages of FHIR Questionnaires in most scenarios.

Provide Feedback

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.

Sharing FHIR Questionnaires

FHIR Questionnaires can be shared between implementations, as their design does not depend on other FHIR resources or external information models. In this way, it is possible to create a repository of FHIR Questionnaires that anyone can access. In the case of Cancer Synoptic Reporting, one group can take the responsibility of transforming the paper-based forms into FHIR Questionnaires and making them available for the community of users.

As an example, the SNOMED International Synoptic Cancer Reporting Clinical Reference Group maintains a GitHub repository with FHIR Questionnaires in: https://github.com/IHTSDO/cancer-synopting-protocol-fhir-questionnaires

This repository records all versions of the questionnaires, with the ability to identify changes and download them for local implementations. Using the collaborative tools in the GitHub repository, it is possible to fork the repository to introduce local customizations, report and discuss bugs and problems in the "Issues" section, and propose questionnaire improvements or changes using the pull request feature.

Rendering Questionnaires in a Clinical Application

It is possible to dynamically create data entry user interfaces based on questionnaire definitions, introducing rich and flexible data capture in clinical applications. Some ready-made open-source libraries can simplify incorporating these functions in clinical software.

This is an example of a Prostate Cancer Form rendered using an open-source library:

The allow the safe incorporation of a questionnaire rendering "plugin" into commercial clinical software. These plugins are executed locally on the client servers and don't share information with external parties.

The National Library of Medicine of the US has published an example SMART Application that can be adapted to render any questionnaire using this standard:

A clinical application records diagnoses, observations, and procedures. Capturing the same information using a FHIR Questionnaire can lead to model duplication, which risks inconsistencies and complicates data retrieval and analytics later. FHIR Standards proposed by the Structured Data Entry group can help minimize these risks and facilitate the integration of the questionnaires with the rest of the information model.

The use of automatic population helps to reduce the pain of having to fill in the same information 'yet again' by allowing a form to automatically fill in answers already known to the EHR or other data source. The user can then verify that the information is still correct (and revise if necessary) rather than needing to fill out the information all over again (and possibly accidentally omitting or incorrectly entering some data).

Data extraction procedures allow data captured in a QuestionnaireResponse to be extracted and used to create or update other FHIR resources, enabling the data to be more easily searched, compared, and used by other FHIR systems.

Read more about the Structure Data Capture guides .

FHIR Questionnaires contain direct references to SNOMED CT concepts as the codes for questions or responses and references as part of ECL expressions. With each new release of SNOMED CT, it is necessary to validate all referenced content to detect inactivations and make any necessary replacements.

SNOMED International provides a tool for validating terminology bindings, allowing FHIR Questionnaires to be uploaded and inactive codes to be replaced using historical associations published with each release.

In the realm of oncology, the standardized capture of diagnostic, treatment, and outcome data is paramount. FHIR (Fast Healthcare Interoperability Resources) provides a compelling solution through its Questionnaire resource, tailor-made to document information in a structured and interoperable manner. Recognizing the critical role of synoptic reporting in cancer care—a method that ensures consistent and comprehensive documentation—we've compiled a set of FHIR Questionnaire examples specifically geared towards cancer synoptic reporting.

These examples demonstrate how the FHIR framework can be adeptly employed to encapsulate vital oncological data, ranging from tumor characteristics to treatment modalities. For oncologists, pathologists, and health IT professionals aiming to enhance the quality and consistency of cancer reporting, these examples serve as both a guide and a starting point for technical implementation.

FHIR Questionnaires

These FHIR Questionnaires can be downloaded as JSON files and viewed and edited using any compatible editor. We recommend using the NLM Form Builder (https://lhcformbuilder.nlm.nih.gov/).

Also, SNOMED International provides a utility to store questionnaires in a standard FHIR server and to validate the SNOMED Terminology Bindings to manage updates of SNOMED versions:

https://ihtsdo.github.io/sct-implementation-demonstrator/#/questionnaires

Audience

SNOMED CT is a comprehensive, multilingual clinical terminology that can be used to standardize and improve the quality of data related to cancer synoptic reporting. This Cancer Synoptic Reporting Clinical Implementation Guide is targeted at the various stakeholders involved with the implementation of SNOMED CT in this domain:

• SNOMED International Members who are seeking uniform, clear best practices for documenting structured cancer pathology reports, and understanding how SNOMED CT can be applied in this domain

• Clinicians who are interested in understanding how SNOMED CT can support the clinical needs for data collection and acquisition within the field of cancer pathology reports for patient care.

• Information managers who are looking to learn how SNOMED CT can be integrated into health information models within the domain of cancer pathology and cancer care to support the implementation of SNOMED CT and enhance data interoperability.

• Software developers who want to learn how to integrate SNOMED CT into software applications used in the domain of structured cancer pathology reporting.

The objective of this Cancer Synoptic Reporting Clinical Implementation Guide is to provide instruction and guidance regarding the SNOMED CT content produced by the Cancer Synoptic Reporting Project Group. The guide provides instructions on implementing SNOMED CT for use in cancer synoptic reporting. After review of this guide, the reader will have the knowledge to implement SNOMED CT encoded cancer synoptic reports for use in the electronic health record, for electronic transmission, and for use in analytics.

The scope of the Cancer Synoptic Reporting Project Group was specific to the creation of SNOMED CT content necessary to unambiguously represent the data elements required for cancer reporting for all solid tumors, adult and pediatric, as published by the College of American Pathologists (CAP) and the International Collaboration on Cancer Reporting (ICCR). The ICCR is supported by the CAP, RCPath, and RCPA as well as other societies of pathology. As a result, data sets produced by the Royal College of Pathology (RCPath) and the Royal College of Pathology Australasia (RCPA) were also used as references for this work. Data sets produced by the ICCR are open source and are now the foundation for the data sets used throughout SNOMED International Member Nations in Europe and Australasia. The protocols produced by the CAP are used in the United States and Canada and are required for laboratory certification. It is estimated that there is a 95% overlap of content between the CAP and ICCR, thus making these two protocol providers reasonable foundations for this project.

As noted, the content addressed in this guide is specific to the structure pathology reporting of malignant neoplasms as specified by the CAP and ICCR. The content created is intended to represent the specific observations made and reported by the pathologist during the examination of excised tissue. It is NOT intended to define the clinical interpretation of the data. Indeed, it is expected that the pathologist and clinicians using the pathology report understand the clinical meaning of the data as contained within any particular report. For example, the criteria to differentiate between an adenocarcinoma and a mucinous carcinoma in the colon versus the breast is expected to be understood by the data creator (pathologist) and user (surgeon or clinician). It is not reflected in the SNOMED CT concept.

The Cancer Synoptic Reporting Project Group highly leveraged the work of the Observables Project Group and used the observable entity hierarchy for much of the new SNOMED CT content developed in this project. This decision was made for three specific reasons:

1. Synoptic reports are structured as a series of tumor features to be observed and the subsequent observation. The use of observable entities to describe the "thing" being observed or measured is consistent with the definition of the observable entity hierarchy.

2. The context for the synoptic data elements is reflected in specific observations to be made by the pathologist. The observations, or answers, to the feature of the neoplasm being observed are often repeated across protocols. (For example: Present, Absent, Adenocarcinoma, Carcinoma, etc). To unambiguously represent all content for all forms of solid tumor protocols would require a substantial number of new concept definitions in many SNOMED CT hierarchies that would exceed the number of Observable entity concepts needed to represent the same data elements.

3. Legacy content as inherited as part of the creation of SNOMED International and the merger of SNOMED RT and the READ codes, was found in both the observable entity and clinical finding hierarchies. SNOMED CT content in either hierarchy was exclusively primitive without concept definition. Substantial changes to the clinical finding hierarchy concept model would have been required in order to create necessary and sufficient concept definitions for these findings. Furthermore, new finding concepts for each tumor type would be necessary to meet the objective of this project as well as the observable entity hierarchy.

Content included in this project consists of:

1. All required data elements for adult and pediatric solid tumors as specified by the CAP and ICCR

2. Biomarker data elements for immunohistochemistry

Content to be further developed:

1. Biomarker data beyond immunohistochemistry, for example, fluorescent in situ hybridization

2. Reporting protocols used for Central Nervous System neoplasms and Hematolymphoid tumors

3. Cancer screening protocols

Content NOT included in this project:

1. Cancer disorder modeling

2. Data elements not explicitly enumerated in published structured pathology reporting protocols

3. Histology modeling quality improvement (separate but related project)

Pathology reports for cancer diagnosis and prognosis are increasingly structured in synoptic form, following guidelines from esteemed organizations such as the College of American Pathologists, the Royal College of Pathology, and others. These reports, guided by national and international protocols, ensure consistency and accuracy across various entities involved in cancer care.

These structured reports, often referred to as data sets, maintain high consistency among different publishing entities. It's imperative to represent the data elements within these reports in both human-readable and machine-readable formats. Computable data elements enable integration into electronic health records for clinical support and seamless transmission to cancer registries for public health purposes, enhancing clinical translational research.

However, prior to 2020, the availability and clarity of SNOMED CT content for cancer synoptic reporting were lacking. Studies by the US Centers for Disease Control and Prevention in 2005 and 2009 highlighted the inadequacy of SNOMED CT and LOINC in encoding cancer data unambiguously for reporting purposes.

Recognizing this deficiency, the Cancer Synoptic Reporting Project Group was established in 2020 with a specific aim: to develop comprehensive SNOMED CT concepts suitable for structured pathology reports. Their objective encompasses supporting clinical, public health, and research applications. Specifically, they aim to create SNOMED CT content necessary for structured reporting across all solid tumor protocols, including those tailored for pediatric cases, as published by leading pathology organizations.

This SNOMED CT Clinical Implementation guide and the underlying work have been developed by the Cancer Synoptic Reporting Project Group. This Clinical Project Group (CPG) is composed of experts in the field of pathology providing input from the community of practice on the development, maintenance, and use of SNOMED CT in this specific domain. The CPG members have been instrumental in the development of this guide, providing their expertise, knowledge, and experience to ensure that it is accurate, up-to-date, and relevant to the needs of its intended audience. Their dedication and hard work have made this guide possible and SNOMED International is is grateful for their contributions. This guide is a product of SNOMED International's ongoing commitment to improving healthcare through the use of high-quality, standardized clinical terminologies.

Main contributors

• W. Scott Campbell, PhD, MBA - Chair, University of Nebraska Medical Center, Omaha, Nebraska, USA

• James R. Campbell, MD - University of Nebraska Medical Center

• Laszlo Igali, MD - Norwich University, UK

This SNOMED CT Clinical Implementation Guide is designed to provide guidance for the use of SNOMED CT within the domain of allergies, hypersensitivity, and intolerance. The guide is organized into five main chapters:

• Chapter 1: Introduction - This chapter provides a background on the guide, including the objectives, scope, and target audience.

• Chapter 2: Clinical Use Cases - This chapter describes the key use cases that have motivated the creation of this guide and explains scenarios where implementation of SNOMED CT within this domain is needed.

• Chapter 3: Content in SNOMED CT - This chapter describes how SNOMED CT addresses the terminological needs within the domain of Cancer Synoptic Reporting.

This SNOMED CT Clinical Implementation Guide represents the culmination of work started by Scott Campbell and James Campbell in 2014 and continued by the Cancer Synoptic Reporting Project Group in 2020.

We welcome feedback from readers on the Guide and encourage them to share their insights and experiences with us. Your comments and suggestions will help us improve the content of the Guide and ensure that it is relevant and useful to those who use it. We will review any feedback received and make updates to the Guide as needed.

We appreciate your interest in this Guide, and thank you for your contributions to the improvement of healthcare through the use of high-quality, standardized clinical terminologies like SNOMED CT. Please raise any comments on this document by emailing , and please mark your response "Cancer Synoptic Clinical Implementation Guide".