Standards for pathology reporting of cancer are foundational to national and international benchmarking, epidemiology, and clinical trials, with international standards for pathology reporting of cancer being undertaken through the International Collaboration on Cancer Reporting (ICCR).
To develop standardized templates for brain tumor diagnostic pathology reporting.
As a response to the 2016 updated 4th edition of the WHO (World Health Organization) Classification of Tumours of the Central Nervous System (2016 CNS WHO), an expert ICCR committee developed data sets to facilitate reporting of brain tumors that are classified histologically and molecularly by the 2016 CNS WHO; as such, this represents the first combined histologic and molecular ICCR data set, and required a novel approach with 3 highly related data sets that should be used in an integrated manner.
The current article and accompanying ICCR Web site describe reporting data sets for central nervous system tumors in the hope that they provide easy-to-use and highly reproducible means to issue diagnostic reports in consort with the 2016 CNS WHO.
The consistent use of these templates will undoubtedly prove useful for patient care, clinical trials, epidemiologic studies, and monitoring of neuro-oncologic care around the world.
The value of a structured or synoptic approach to cancer reporting, leading to improvement in the quality and completeness of pathology cancer reports, has been recognized through many studies1–4 and the colleges of the United Kingdom, Australia, and the United States, and many other centers around the world have engaged in the development of national or local standards as a result. However, while each of these local standards often uses the same cohort of evidence as its basis, each is constructed differently and uses different terminology, and similar elements may be based on different methodologies, and they are therefore not comparable.
The US (College of American Pathologists [CAP]), Australasian (Royal College of Pathologists of Australasia), and UK (Royal College of Pathologists) Colleges of Pathology and the Canadian Association of Pathologists-Association canadienne des pathologistes, in association with the Canadian Partnership Against Cancer, recognized the value of agreed international standards and in 2011 a formal collaboration commenced: the International Collaboration on Cancer Reporting (ICCR). This initial collaboration addressed the development of reporting standards for 4 cancers: lung, melanoma, prostate (radical prostatectomy), and endometrium.5 Each was undertaken by an expert committee with representatives from each of the 4 countries. The results were extremely positive and encouraging, and in 2013 the collaboration expanded to include the European Society of Pathology and these 5 organizations became the founding members of the ICCR, which was incorporated as a not-for-profit organization in late 2014. The ICCR continues to expand its membership and affiliations with like-minded organizations from around the world.
The ICCR data sets are made freely available for use by organizations and individuals globally. It is anticipated that, in time, this will enable the alignment and normalization of pathology cancer data around the world as producers of data sets adopt and incorporate the ICCR data sets.
The identification and classification of tumor types is essential to the pathology reporting of cancer and is a feature of all ICCR data sets. The International Agency on Cancer Research (IARC) is responsible for the development and publication of the World Health Organization Classification of Tumours series (“WHO Blue Books”), which is a vital resource for worldwide pathology reporting of cancer. In 2013, the ICCR agreed to synchronize its schedule of data set development with the publication of the WHO Blue Book series. In 2016, the IARC released the updated 4th edition of the WHO Classification of Tumours of the Central Nervous System6 (2016 CNS WHO), and as a result, the ICCR commenced development of a data set to align with this publication.
The process followed the Guidelines for the Development of ICCR Datasets7 (http://www.iccr-cancer.org/datasets/dataset-development; accessed March 1, 2019). This development framework dictates the process as well as the format and the content of the data sets.
Key to the success of the development of an international standard such as the ICCR data sets is the selection of a suitably qualified chair and Data Set Authoring Committee (DAC). Committee members were chosen primarily for their expertise in CNS tumors but also with the goal of achieving a committee with both geographic and practice diversity. In-depth familiarity with the updated WHO Classification of Tumours of the Central Nervous System6 was essential. The DAC was composed of 14 pathologists/neuropathologists and 1 clinician (MW) and supported by an ICCR representative (BR) and project manager (MJJ); in this case, because CAP was updating its CNS reporting protocol, a representative of the CAP CNS group was also included (EMH).
ICCR data sets are composed of 2 types of elements: core and non-core. Core elements are defined as those that are unanimously agreed upon by the panel to be mandatory for diagnosis, clinical management, staging, or prognosis. Non-core elements are nonmandatory and are defined as clinically important and recommended as good practice; they should ideally be included in the report but may not yet be validated or regularly used in patient management. Evidentiary support at level III-2 or above8 is required to support core elements; however, where level III-2 evidence is not available (a common situation for CNS tumor classification), an element was categorized as core with unanimous agreement of the DAC. Commentary such as explanatory text, diagrams, or tables is added where necessary to clarify the elements: to define the way an item should be reported, to ensure clarity and conformity, to explain why an item is included (eg, how an item assists with clinical management or prognosis of the specific cancer), to cite published evidence in support of the element, and to state any exceptions or issues that may be encountered by the reporting pathologist. Detailed commentary is designed to provide contextual guidance to the reporting pathologist.
A working draft was initially developed by the chair and formatted into a voting document for distribution to the DAC. Feedback from the DAC was compiled and provided a basis for discussion for each of 3 Web/teleconferences. A further 3 detailed surveys were undertaken to garner opinion from the DAC on a variety of issues. Members of the DAC were appointed to provide further information on specific elements that then formed the basis of commentary. Once completed and endorsed by the DAC, the data set was formatted and posted to the ICCR Web site for a period of 2 months for public comment. Following this feedback, the data set was reviewed, and final changes were made by the DAC. After final review by the DAC, the data set was approved for publication and submitted to the ICCR Data Set Steering Committee for ratification.
The CNS data set has been developed for the pathology reporting of benign and malignant tumors of the CNS and its coverings, as well as tumors from those aspects of the peripheral nervous system immediately adjacent to the CNS. The data set applies to both biopsy and resection specimens. Tumors of the anterior pituitary gland and hematologic lesions that may originate in the CNS are included.
The DAC agreed that, per the recommendations in the 2014 ISN (International Society of Neuropathology)–Haarlem guidelines,9 a pathology report format should consist of 4 layers: Layer 1: Integrated diagnosis (incorporating all tissue-based information); Layer 2: Histologic classification; Layer 3: WHO grade (reflecting natural history); and Layer 4: Molecular information.
To accomplish this, the CNS ICCR data set has taken a different approach from prior ICCR data sets in that 3 interrelated data sets were generated. The 3 data sets are as follows: (1) Histological assessment of CNS specimens (including both layers 2 and 3, ie, histologic classification and grade); (2) Molecular information for CNS specimens; and (3) Final integrated report/diagnosis for CNS specimens.
Importantly, it is strongly recommended that these data sets be used together for tumors in which molecular information is captured in their diagnosis, resulting in an integrated report/diagnosis. A full diagnosis of CNS tumors should ideally conform to the 2016 CNS WHO, which requires integration of elements from histologic and ancillary analyses. However, because most 2016 CNS WHO entities can be diagnosed solely on the basis of histologic features, in many situations, only the histologic and final data sets need to be completed. Thus, the molecular assessment (whether nucleic acid or protein based) does not need to be completed for those tumors in which molecular information is not captured for diagnostic purposes. Nonetheless, diagnostic molecular data are being used to diagnose a growing subset of CNS tumors and it is anticipated that use of such data will further increase over time; for this reason, the importance of molecular data sets and integrated diagnoses is likely to increase as well over time. Lastly, taking into account that the ICCR data sets are intended for use throughout the world, this sectional approach to the data set allows the histologic assessment to be used standalone in the event that molecular testing is not available or failed.
For prior ICCR data sets, the accompanying journal article has essentially replicated the data set, including all of the detailed commentaries. For the CNS ICCR data sets, that approach would not be practical, given the multiple data sets and the length of the explanatory commentary. For this reason, we have chosen to highlight only selected aspects herein, and the reader is instead directed to the on-line data sets for the full details.
Notably, for the CNS data sets, the discussion as to whether an element was core or non-core often became complex, with different opinions expressed that reflected the customs at multiple institutions around the world. The resulting data sets therefore only included 2 core elements: specimen dimension and histologic grade. In general, this decision did not reflect an underlying opinion that the “non-core” data elements were not important, but rather that reasons could typically be found why nearly all of these elements may not always be present in pathology reports of CNS tumors. The distinction between core and non-core is therefore not of primary importance for the CNS data sets.
The elements of the histologic data set are listed in Table 1, with the corresponding detailed commentaries provided in the on-line data set. To guide histologic grading of the more common CNS tumors, Tables 2 through 4 are provided here, in particular for the common diffuse astrocytic gliomas and the meningioma, with commentary to be found on-line. These guidelines are current as of the workings of the DAC in mid-2018 but do not fully include more recent published suggestions that could affect grading in future WHO classifications.10–12
The elements of the molecular data set are shown on the left in Table 5, with the table guiding the pathologist in determining which molecular tests are required or recommended, either for classification and/or differential diagnosis. It is anticipated that such a table could change fairly quickly over time. The designations are divided into those markers that are components of the 2016 CNS WHO diagnostic criteria and 2017 WHO diagnostic criteria for pituitary adenomas (designated as “W”); those that are commonly used to support or refine the diagnosis, or provide important ancillary information in the corresponding tumor type (designated as “D”); those that are commonly used to rule out the diagnosis (designated as “D*”); those that can also be used to support or refine the diagnosis, or provide important ancillary information in specific tumor subtypes (designated as “(D)”); see commentary for details. As mentioned above, it is likely that molecular parameters will change fairly quickly over time and therefore there is a section for Other Findings that should be used for documenting results for other genetic alterations and/or for molecular results in other tumor types, such as metastases and hematologic lesions. Once again, extensive details concerning each of these molecular parameters are provided on-line, and an example of how molecular data can contribute to a diagnosis is given for medulloblastoma in Table 6.
Table 7 provides the current 2016 CNS WHO classification, which forms the basis for the Integrated Diagnosis data set. All reports should strive to render a diagnosis from the 2016 CNS WHO,6 although it is recognized that this may not be possible in all instances (ie, that more descriptive diagnoses may be needed for tumors that do not meet criteria for 2016 CNS WHO entities).6,13 In many situations, 2016 CNS WHO diagnoses “integrate” histologic and molecular information and have been referred to as “integrated” diagnoses; for these entities, both histologic and molecular information is needed. (In this context, “molecular information” refers to data from any type of molecule [eg, DNA, protein], so that an immunohistochemical test provides “molecular information.”) In some scenarios, there may be differences between histologic appearance and 2016 CNS WHO diagnosis (eg, a diffuse glioma without overt oligodendroglial features but with IDH mutation and 1p/19q codeletion). Moreover, in other scenarios, necessary molecular information may not be available, leading to one of the “not otherwise specified” (NOS) 2016 CNS WHO diagnoses.
It is important to keep in mind that most 2016 CNS WHO entities can be diagnosed solely on the basis of histologic features. While for such entities the diagnosis may be identical to the histologic appearance (eg, choroid plexus tumors), for others there may be differences (eg, a diffuse glioma with an integrated diagnosis of “diffuse astrocytoma, IDH-mutant” that has a histologic appearance that is not fully or classically a diffuse astrocytoma yet has a characteristic astrocytic genotype—IDH1, ATRX, and TP53 mutations as well as 1p/19q retention). In the latter type of case, layered reports (see above)9 have most value in distinctly conveying such findings.
“Diagnosis not elsewhere classified”: In the event that all diagnostic information is present but the tumor still does not meet criteria for an entity defined by the 2016 WHO classification (eg, a pediatric diffuse glioma that does not harbor IDH or H3 mutations), a “descriptive” or NEC (not elsewhere classified) diagnosis can be issued, which draws attention to the unusual nature of the lesion. Such designations are distinct from NOS diagnoses, which are included in the 2016 WHO classification and that are cases in which necessary diagnostic information is not available.13
DISCUSSION AND SUMMARY
The 2016 WHO Classification of CNS Tumors6 differs from the prior, 2007 classification in that it not only incorporates some new entities and deletes old ones, but also formulates a number of common diagnoses in terms of both histologic and molecular parameters. Having diagnostic terms based on both histology and molecular analysis has, however, created a set of challenges for pathologists: How does a pathologist make 2016 CNS WHO diagnoses in a setting in which molecular assays are not available? How does a pathologist display the histologic and molecular findings in a way that is most accessible and understandable to clinicians, patients, and researchers seeking to use these diagnoses? How does a pathologist produce an initial diagnostic report in advance of molecular findings being ready, and then adjust that report once the molecular findings are generated—particularly in settings in which the molecular results may take weeks?
The 2016 CNS WHO Blue Book addressed the first of these challenges through the creation of NOS entities. Such diagnoses were intended to be used in those situations in which molecular assays were either not available or did not generate usable results. They have proved useful in allowing WHO diagnoses in resource-challenged settings. Most importantly, they in turn provide a “red flag” to an oncology center when a patient presents for treatment with such a diagnosis, hopefully encouraging molecular workup at that time. And, while NOS diagnoses have generated questions as to their best use, clarifications have already come out of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) effort.13 Similarly, cIMPACT-NOW has generated other sets of recommendations that are reviewed in detail elsewhere and in the Notes accompanying the on-line ICCR data sets11,14
The second and third of the abovementioned challenges—those relating to reporting formats—were not addressed in the 2016 CNS WHO Blue Book but are considered here as part of the ICCR. Notably, a key element of the ICCR approach is based on the guidelines issued from the ISN-Haarlem meeting held in 20149: a layered report. A layered report provides the diagnosis in a stereotypically layered format (see above), which readily allows visualization of histologic and molecular findings, as well as the final or “integrated” diagnosis that corresponds to the 2016 CNS WHO classification. To do so, the ICCR committee has created and recommends the use of 3 separate data sets for histologic, molecular, and integrated components of the report. In addition, the layered report and separate data sets also more readily allow modification of the molecular and integrated sections once molecular results are available.
Addressing these challenges required a DAC that had, in addition to neuropathology expertise, input from clinical neuro-oncology and general pathology. It also required relaxing the criteria regarding so-called core elements versus non-core elements, and it may be that as other organ systems incorporate molecular markers into classifications, the distinction between core and non-core elements needs to be revisited.
These 3 data sets should be used together, with the histologic and molecular data sets contributing to the final integrated report/diagnosis. Nonetheless, because most 2016 CNS WHO entities can be diagnosed solely on the basis of histologic features, in many situations, only the histologic and final data sets will need to be completed. It is anticipated that fewer diagnoses will be amenable to histology-only classification, but complete transition to combined histologic-molecular classification may take a long time—or may never happen given the relative ease and low cost of histologic diagnosis. The current data sets are therefore flexible and can be used for either histologic-molecular or histology-only reporting of CNS tumors, whether molecular testing is not needed or not available.
In conclusion, the current article and accompanying ICCR Web site16 present reporting data sets for CNS tumors in the hope that they provide easy-to-use and highly reproducible means to issue diagnostic reports in consort with the 2016 CNS WHO. The Notes that clarify the data sets in turn provide extensive practical guidance to pathologists in areas that range from clinical to histologic to molecular. The consistent use of these templates could prove extraordinarily useful for patient care, clinical trials, epidemiologic studies, and monitoring of neuro-oncologic care around the world.
We acknowledge the financial contribution of Massachusetts General Hospital toward the production of this data set.
The authors have no relevant financial interest in the products or companies described in this article.