Structured Reporting in Anatomic Pathology for Coclinical

Topic Introduction

Structured Reporting in Anatomic Pathology forCoclinical Trials: The caELMIR Model

Robert D. Cardiff,1 Claramae H. Miller, Robert J. Munn, and Jose J. Galvez2

Center for Comparative Medicine and Center for Genomic Pathology, University of California, Davis,Davis, California 95616

Electronic media, with their tremendous potential for storing, retrieving, and integrating data, are anessential part of modern collaborative multidisciplinary science. Structured reporting is a fundamentalaspect of keeping accurate, searchable electronic records. This discussion on structured reporting inanatomic pathology for pre- and coclinical trials in animal models provides background informationfor scientists who are not familiar with structured reporting. Practical examples are provided using aworking database system for preclinical research—caELMIR (Cancer Electronic Laboratory Manage-ment Information and Retrieval)—developed by the U.S. National Cancer Institute’s (NCI’s) MouseModels of Human Cancers Consortium (MMHCC).

INTRODUCTION

Genetically modified mice, created for the study of cancer, have become the cornerstone of experi-mental cancer research. Genetically engineered mouse models (GEMMs) provide the modern equiv-alent of “Koch’s Postulates” for testing and verifying the role of a gene in cancer biology (Begemannet al. 2002). Important concepts, such as oncogene addiction and plasticity, have developed from thestudy of GEMMs (Cardiff et al. 2011). They have been central to research on cancer initiating cells(Cardiff et al. 2011). These animals are now being used for studies on the prevention and treatment ofcancer (Abate-Shen et al. 2008; Nardella et al. 2011; Chen et al. 2012).

The advent of these large multi-institutional cooperative cancer research trials requires the use ofelectronic media and data sharing (Birney et al. 2009; Schofield et al. 2009). Without electronic media,the accumulation, storage, retrieval, and analysis of data would be cumbersome at best and impossibleat worst. Thus, it becomes incumbent upon the individuals involved in modern research to have arudimentary understanding of fundamental requirements for modern data storage that facilitatesharing of scientific data.

For individualbench scientists,watching their relatively simple “reductionist” experimentdivided intonumerous parts and parsed out to various “omic” scientists and returned as “systems biology” can be asomewhat bewildering experience (Hoehndorf et al. 2011b). However, most scientific data, includingdocumentationofanatomicpathology,arenowtransmittedandanalyzedelectronically. If thecreatorsoftheGEMMs, thebenchscientists, are to remainan importantpartof the“team,” theywillneed tobecomefamiliar with electronic reporting formats. Data in the formof structured reports and controlled vocab-ulary are essential for proper operational use of any anatomic pathology standard operating procedure.

1Correspondence: [email protected] address: National Cancer Institute, Informatics Operations Branch, Rockville, Maryland 20850.

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Each collaborating scientist needs to understand how electronic reports are structured and whythey are used. A basic understanding can be gained from the caELMIR model developed under theauspices of the NCI’s MMHCC for use in preclinical trials and modified for local use in severallaboratories.

caELMIR: THE BACKGROUND

An informal survey commissioned by the MMHCC of five laboratories chosen for their use ofGEMMs showed that none were using any type of integrated electronic system to collect, store, orretrieve data. Most phenotype “pathology” data consisted of postprocessed, interpretive free-textnarratives. Our subsequent experience with basic research laboratories throughout the world hasconfirmed these survey findings. The research community has made almost no effort to collect andintegrate their phenotype or pathology information with other data sources.

Thus, although almost all research laboratories use computers for capturing, storing, and retriev-ing data, relatively few laboratories have systematic mechanisms for sharing the data throughout theirinstitution or with colleagues in other institutions. Furthermore, the lack of controlled terminologiesand appropriate metadata leads to difficulty in interpreting any shared data (Schofield et al. 2010a).Most data communicated outside the laboratory—and even within the laboratory—primarily com-prised representations or interpretations of the raw data (e.g., images, spreadsheets, or word-proces-sor documents). Furthermore, such representations are not searchable and are rarely filed withstandard names (terminology) or metadata. As a result, laboratory filing systems are usually individ-ualized and chaotic, without central oversight or control. This type of computer usage dilutes the valueof the collected data and is entirely inappropriate for the rapidly emerging large-scale national andinternational cooperative organizations.

The success of regional “Mouse Hospitals” envisioned for global prevention and therapeutic trialsinvolving mice (Chen et al. 2012) will require a much more rigorous use of searchable controlled andscientific validated data elements. Therefore, the scientific community needs to establish and usestructured and integrated pathology reports with controlled terminologies (Cardiff et al. 2004).Without such reports, the comparison and interpretation of results will be impossible. In early2003, we were commissioned by the NCI’s MMHCC to develop an appropriate database. A prototypedatabase, caELMIR, specifically designed for use with experimental animal research, was produced(Cardiff et al. 2004). caELMIR has subsequently been modified and adapted for the Aperio Spectrum(eSlide Manager) database for experimental pathology. It has been tested and refined over the last8 years and currently holds more than 30,000 cases.

STRUCTURED REPORTS

Clinical trials in human medicine have used “synoptic” reporting for more than 30 years to capturescientifically validated data elements (SVDEs). These “synoptic reports” provide a summary (synop-sis) of essential elements (SVDE) using standard nomenclature. The SVDE and structured reportsprovide a cued, or prompted, report form that assures that the observer records all importantinformation and provides the clinical scientist with uniform, interpretable data elements. With theadvent of electronic media, the optimal use of this modern computer technology works best withcontrolled vocabulary (Cardiff et al. 2004).

Computer programs are based on binary numbers and, as such, have no “understanding” of themeanings we give to objects (Schofield et al. 2010a; Hoehndorf et al. 2011a). The meanings are“modeled,” or represented, within a given program or data construct to fit our concepts and usingrelational data tables. Data tables (behind the data fields the user sees) are constructed to capturecritical data (SVDE) within a preconceived organizational structure (ontology). This preconceived

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Reporting in Anatomic Pathology for Coclinical Trials




organization or model gives meaning to the data represented within the computer. The challenge is toensure that the user responds with the appropriate SVDE. One way to provide the expected parameteris to limit the possible choices through a “cued,” or prompted, checklist that is restricted to a series ofpossible responses in the given field. These seemingly rigid structures have the advantage that theresponses maintain specific syntactic meaning and can be easily searched, retrieved, and tabulated.They have been used for many years in clinical medicine as synoptic, or structured, reports designed tocapture the information needed for analysis.

Structured report forms are built around ontologies that are hierarchical (multitiered) systems forrepresenting information using formal Aristotelian logic where every entity has “is_a” relationshipswith higher levels (is_a child of) or (is_a parent of) of a lower entity (the child). Similar entities at thesame level may be siblings. In the ontology outlined below, the general structure of the databaseincludes an enforced hierarchy of Studies, Experiments, Cohorts, Sub-Cohorts, Specimens, andSamples (Slides). For understanding this discussion, the reader is reminded that the “Specimen” isthe individual animal. In this format, all samples belong (is_a child of) to an animal (is_a parent of),and all animals belong (is_a child) to subcohorts. These, in turn, belong to cohorts, which belong toexperiments, and so forth. In this ontology, many lateral parallel relations can exist. For example, aStudy can have many Experiments (children). Each experiment (as parent) can have many Cohorts.

By adhering to this type of construct, each mouse has a unique identifier that allows it to havemembership in a specific Cohort, Experiment, or Study. If these simple rules are followed, the mouseand its relationships with other mice, studies, and its own phenotype data will never be lost. Thesegoals are accomplished by the computer using interactive relational tables. The user simply sees ablank field with a title defining the type of SVDE needed. The trick is to fill out the field with thecorrect words or numbers (controlled vocabulary).

CONTROLLED TERMINOLOGIES

Understanding what the user means when a specific term is entered becomes a critical issue. Forexample, situations are commonly encountered among surgical (medical) and veterinary pathologistswhere the same word is used tomean different things (polysemy), or where different words are used tomean the same thing (synonymy), leaving the other discipline confused. The tendency for individualsto use different terms to describe a similar concept is well established and is described as “thevocabulary problem” (Chen 1994). For example, is a keratoacanthoma amammary tumor (veterinarypathology) or a skin tumor (human dermatopathology)? Is an EMT tumor the same as a carcino-sarcoma and a claudin-low tumor?

Controlled terminologies maintain conceptual meaning while tracking the ever-changing use ofspecific terms or synonymy. The preservation of the information’s semantic integrity has high pri-ority, because, if the information is to be reusable by anyone other than the original recorder (theprincipal investigator, the necropsy assistant, or the pathologist), it must be understood, and in thelong term this is only accomplished through the use of well-maintained, controlled vocabularies.

If humans cannot understand each other, one can imagine how difficult it is for the computer tointerpret the terms used by humans. Investigators tend to use “lab jargon” as shorthand substitutes forgenetic nomenclature (Sundberg and Schofield 2010). For example, the terms TRAMP, LADY, andT121 have been used to designate three different transgenic models using different forms of the SV40 Tantigen. People familiar with the field know the differences in the molecular constructs, but it is notobvious to your computer. A similar problem is with the designation of strain background. The 129strain is a favorite for GEMMs, but 129mice have at least seven different coat colors. So, it is a complexmixture of genetic contamination. Without this knowledge, the computer and the naïve scientist canget very confused. However, when the correct designation is used, the animal is clearly understood.For example, 129S6/SvEvTac designates a mouse from specific colony of 129 from Taconic.

The solution to these problems is to insist on a controlled vocabulary. A controlled vocabulary is acollection of concepts characterized by terms with defined meanings. If the vocabulary includes

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R.D. Cardiff et al.




synonyms (synonymy functionality), then different vocabulary words may be used as “linguisticlabels” to represent the same concept. The “labels” do not define the concept; rather, the conceptis defined by its “definitional criteria” in the form of specific attributes or metadata (characteristics)and their respective values. The concept-basedmechanism for describing and exchanging informationhas been used over three decades.

In informatics terms, description logicuses a logic-based formalismwith constructors thatprovide asemantically consistent means of defining concepts and composing new ones from existing “atomic”concepts.Description logic is used to support information retrieval anddecision support systems and tomanage the classification itself. The Systematized Nomenclature of Human and VeterinaryMedicine–Clinical Terms (SNOMEDCT) (Spackman et al. 1997) is based on description logic. Mouse PathologyOntology (MPATH) (Sundberg et al. 2008; Schofield et al. 2010b) provides an extensive ontology usingcontrolled vocabulary that can be mapped across species and linked to other terminology sources.

Description logic uses definitions to formalize classifications. With a given concept, there may besynonymous terms as well as preferred terms. The definition should include general statements abouttheir relationship to other similar concepts (genera) as well as statements that distinguish them fromother concepts (differentia). The definitions should be in the so-called “is_a” format that defines therelation of a concept within a hierarchy. If the terms used by the veterinary pathologist do notcorrespond to anything that the surgical (medical) pathologist uses, parent terms (i.e., at a higherlevel on the hierarchical tree) may be used, or the terms may be defined as synonyms. Otherwise, theterms must be defined so that the computer can use them.

Ideally, each definition is composed of a set of attributes, each with allowable values (constraints)and logical operands that may disallow combinations of concepts. The use of logic constructs forattributes is a fundamental core of description logic and enables a certain inference (e.g., the “class”male cannot have organ_uterus). This inference can be used to aid in the classification of new entities aswell as aggregation of entities under larger (parent) classes based on the sharing of attributes.

With an understanding of these concepts, we can now examine their use in an operational model,caELMIR.

EXAMPLES FROM caELMIR

The caELMIR database provides a hierarchical ontogeny based on the design of clinical trials butadapted for experimental pathology. The four main interconnected tables are arranged in a hierarchy(Study, Experiment, Specimens, and Samples). When one considers a clinical trial, the patient (Speci-men) is enrolled in an “arm” of a trial (Study). The patient is considered part of a Cohort or Sub-Cohort of a given “arm.” Depending on the complexity of the trial, the arms may be multiple. Inmulti-institutional trials, one might want to keep the patient results segregated according to eachinstitution. The caELMIR structure mirrors the clinical ontologies but with terms that are suitable formouse informatics.

Each caELMIR table contains multiple defined fields that require entry of SVDEwith controlled orstandard vocabulary. Each field label names the type of data that is required in the field. Ideally, theresponse uses a controlled numerical value or terminology. Most fields are easily defined and con-trolled. The date of specimen submission is an example. However, the format has to be specified. Doesthe user use month/day/year or day/month/year? Or, does the field use numbers or names for themonths and days? If the field uses free text, the dates may appear in multiple forms. In most systems,the computer automatically adjusts the date entered to the desired format. But consider what wouldhappen if the date is free text and a user erroneously enters the wrong word, “male”: The specimenwould not be properly searched by date or gender.

The caELMIR structure also reflects laboratory workflow by allowing entries from the investigator,pathology assistant, histology technician, and pathologist. The information accrued from the firstthree people in the workflow is used by the last (the pathologist) to interpret the histopathology. Oneissue is whether or not the computer and all four participants understand the intendedmeaning of the






words used in the controlled vocabulary. Another issue is whether standard nomenclature is enforcedwhere free text is allowed.

Examples of the use of caELMIR for pathology reports are discussed below. The specific examplesare from the Center for Genomic and Translational Phenotyping (CGTP) Image Archive Aperiodatabase, which uses the caELMIR structure. As previously described, the workflow ontology isbased on a specific set of relationships: Study is_a parent of all Experiments, each Experiment is_aparent of Cohorts (and is_a child of the Study), each Cohort is_a parent of SubCohorts, each Sub-Cohort is_a parent of Specimens, and each Specimen (each animal) is_a parent of each Sample (tissuesample/slide). Rephrased, a Specimen can be a sibling of many Specimens that are children of_a (thesame) SubCohort that is a child of_a Cohort that has many children (SubCohorts).

The Study

The Study is the highest level of experimental pathology. As such, the Study is the parent of all thatfollows. The Study defines the PI’s intentions. The PI is responsible for collecting and entering datathat include the background and description of the study, and the pertinent location and contactinformation of the investigator.

Because the same type of study may be repeated by the same or other investigators, the studyshould include additional information such as a name and/or a time stamp that allows identificationof the study source. It is advisable to use dates and names to provide a “unique identifier” for eachstudy. In the example shown in Figure 1, the Study is identified as “MMHCC III Breast Consensus.”The PI and Description provide additional identifiers as needed.

In Figure 2, note that the order, the data type, and vocabulary are specified. If the field is colored,the data come from another table. The clear fields are free text.

The Experiment

Each Study will have one or more Experiment(s) executed to complete the Study. The PI, or theirsubmitter, is also responsible for providing the SVDE for this section. The key SVDE here is thegenetic information.

Many investigators do not use unique identifiers to identify Experiments; however, it is advisableto use time stamps to identify each Study with the same name. Many laboratories identify andcharacterize their genotypes using convenient internal “laboratory jargon” or nicknames. Ideally,the genes and their mutations should be identified by the formal genetic nomenclature. The fieldsfor Genetic Description and Designation (see Figs. 3 and 4) require a standard vocabulary dictated bythe submitter. For example, the Designation might be “homozygous” and “heterozygous,” and theDescription might use formal genetic nomenclature. In the example shown in Figure 3, this submitter

FIGURE 1. An example of a Study report.


R.D. Cardiff et al.




FIGURE 3. An example of an Experiment report.

FIGURE 2. The table behind the sample Study report in Figure 1.






chose to place the gene description in the Genetic Designation field. Now, to find the information, thesearch will have to involve both fields.

The Specimen

The Specimen holds data about the individual animal. A technical flaw in the Aperio database systemis the limitation of the Aperio system to four tables. As a result, we were unable enforce a stricthierarchy of Study, Experiment, Cohort, and Specimen. Our practical solution, designed to cover themost common contingencies, was to embed the Cohort-SubCohort within both the Experiment (Fig.3) and Specimen (Fig. 5) as “the_child of” in both categories.

The recording of these data generally falls to the pathology assistant, fellow, student at the time ofnecropsy. The person who does the actual necropsy is responsible for providing this information.They can use this page as for a synoptic or structured report. The first portion is details of the specificanimal. The pathologist will need to know genetic and other experimental manipulations. The genderand age of the animal is critical to long-term studies. Any notable gross findings should be recorded.(See the example in Fig. 5.)

In Figure 6, note that six fields have vocabulary fields that are filled out. This is a true controlledvocabulary in which the user must choose one of the possible options.

One potential problem is that the data are incomplete. For example, the histology technician maynot have indicated when a sample was received. It is advisable to develop an automated time stamp,and to audit and correct the data. The later requires a person who is responsible for auditing all entries.

Pathology

Finally, the pathologist is responsible for the microscopic description, diagnosis, and interpretation.Investigators can then enter the database and view their cases individually in the context of the entirestudy or as an individual slide (see Fig. 7). Alternatively, they can download a spreadsheet report toreview the entire collection (see Fig. 8).

FIGURE 4. The table behind the Experiment report in Figure 3.


R.D. Cardiff et al.




FIGURE 5. An example of a Specimen report.






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FIGURE 8. A sample Excel spreadsheet report. The columns extend to AD with various fields (not shown).

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FINAL COMMENTS

The format used here is recommended because it contains the critical SVDEs for conducting pre-clinical or coclinical trials. Without controlled terminologies, the fields can be misused, therebycreating major problems in communications between computers. The majority of our currentfields are free text without required controlled vocabularies. Thus, miscommunication canbe rampant.

These formats for controlled vocabularies are used every day by pathologists and other scientists,but the rules are seldom recognized as a formal vocabulary until the users are required to develop suchsystems for computers. Our general lack of understanding of terminology often leads to confusing andcontradictory statements in our pathology literature. The situation has been further confused bynonpathologists who publish descriptions of pathologic phenomena. Strict adherence to the rulesis, however, essential for computers. If we understand that the computer just needs us to use a littlediscipline with vocabulary, the task is simple and should not be considered onerous.

The current hierarchical structure may not fit the workflow for another institution. The SVDE arethe critical elements. The structure can be altered to fit the need as long as the SVDE can be mapped tothe new organization.

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doi: 10.1101/pdb.top078790 originally published online September 30, 2013Cold Spring Harb Protoc; Robert D. Cardiff, Claramae H. Miller, Robert J. Munn and Jose J. Galvez ModelStructured Reporting in Anatomic Pathology for Coclinical Trials: The caELMIR

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Structured Reporting in Anatomic Pathology for Coclinical