The World Health Organization Reporting System for Pancreaticobiliary Cytopathology: Overview and Summary.

The recently published WHO Reporting System for Pancreaticobiliary Cytopathology (World Health Organization [WHO] System) is an international approach to the standardized reporting of pancreaticobiliary cytopathology, updating the Papanicolaou Society of Cytopathology System for Reporting Pancreaticobiliary Cytology (PSC System). Significant changes were made to the categorization of benign neoplasms, intraductal neoplasms, mucinous cystic neoplasms, and malignant neoplasms considered low grade. Benign neoplasms, such as serous cystadenoma, categorized as Neoplastic: benign in the PSC system, are categorized as Benign/negative for malignancy in the WHO system. Pancreatic neuroendocrine tumor, solid-pseudopapillary neoplasm, and gastrointestinal stromal tumor, categorized as Neoplastic: other in the PSC system, are categorized as Malignant in the WHO System in accord with their classification in the 5th edition WHO Classification of Digestive System Tumours (2019). The two new categories of Pancreaticobiliary Neoplasm Low-risk/grade and Pancreaticobiliary Neoplasm High-risk/grade are mostly limited to intraductal neoplasms and mucinous cystic neoplasms. Low-risk/grade lesions are mucinous cysts, with or without low-grade epithelial atypia. High-risk/grade lesions contain neoplastic epithelium with high-grade epithelial atypia. Correlation with clinical, imaging, and ancillary studies remains a key tenet. The sections for each entity are written to highlight key cytopathological features and cytopathological differential diagnoses with the pathologist working in low resource setting in mind. Each section also includes the most pertinent ancillary studies useful for the differential diagnosis. Sample reports are provided for each category. Finally, the book provides a separate section with risk of malignancy and management recommendations for each category to facilitate decision-making for clinicians.

Moving forward on tumor pathology research reporting: a guide for pathologists from the WHO Classification of Tumors EVI MAP Group.

Evidence-based medicine (EBM) can be unfamiliar territory for those working in tumor pathology research and there is a great deal of uncertainty about how to undertake an EBM approach to planning and reporting histopathology-based studies. In this article, endorsed by the WHO International Agency for Research on Cancer's (IARC) International Collaboration for Cancer Classification and Research (IC3R), we aim to help pathologists and researchers to understand the basics of planning an evidence-based tumor pathology research study, as well as our recommendations on how to report the findings from these. We introduce some basic EBM concepts, the PICO model, thoughts on study design, and emphasize the concept of reporting standards. There are many study-specific reporting guidelines available, and we provide an overview of these. However, existing reporting guidelines perhaps do not always fit tumor pathology research papers, and so here we collate the key reporting dataset together into one generic checklist that we think will simplify the task for pathologists. The article aims to complement our recent Hierarchy of Evidence for Tumor Pathology and Glossary of Evidence (study) Types in tumor pathology. Together, these articles should help any researcher get to grips with the basics of EBM for planning and publishing research in tumor pathology, as well as encourage an improved standard of the reports available to us all in the literature.

A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks.

Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, clinical, and histopathologic characteristics. There are more than 35 different histologic forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch-matching tools, allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the World Health Organization breast taxonomy (Classification of Tumors fifth ed.) spanning 35 tumor types. We visualized all tumor types using deep features extracted from a state-of-the-art deep-learning model, pretrained on millions of diagnostic histopathology images from the Cancer Genome Atlas repository. Furthermore, we tested the concept of a digital "atlas" as a reference for search and matching with rare test cases. The patch similarity search within the World Health Organization breast taxonomy data reached >88% accuracy when validating through "majority vote" and >91% accuracy when validating using top n tumor types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.

A New Hierarchy of Research Evidence for Tumor Pathology: A Delphi Study to Define Levels of Evidence in Tumor Pathology.

The hierarchy of evidence is a fundamental concept in evidence-based medicine, but existing models can be challenging to apply in laboratory-based health care disciplines, such as pathology, where the types of evidence and contexts are significantly different from interventional medicine. This project aimed to define a comprehensive and complementary framework of new levels of evidence for evaluating research in tumor pathology-introducing a novel Hierarchy of Research Evidence for Tumor Pathology collaboratively designed by pathologists with help from epidemiologists, public health professionals, oncologists, and scientists, specifically tailored for use by pathologists-and to aid in the production of the World Health Organization Classification of Tumors (WCT) evidence gap maps. To achieve this, we adopted a modified Delphi approach, encompassing iterative online surveys, expert oversight, and external peer review, to establish the criteria for evidence in tumor pathology, determine the optimal structure for the new hierarchy, and ascertain the levels of confidence for each type of evidence. Over a span of 4 months and 3 survey rounds, we collected 1104 survey responses, culminating in a 3-day hybrid meeting in 2023, where a new hierarchy was unanimously agreed upon. The hierarchy is organized into 5 research theme groupings closely aligned with the subheadings of the WCT, and it consists of 5 levels of evidence-level P1 representing evidence types that merit the greatest level of confidence and level P5 reflecting the greatest risk of bias. For the first time, an international collaboration of pathology experts, supported by the International Agency for Research on Cancer, has successfully united to establish a standardized approach for evaluating evidence in tumor pathology. We intend to implement this novel Hierarchy of Research Evidence for Tumor Pathology to map the available evidence, thereby enriching and informing the WCT effectively.

The rationale for the development and publication of the World Health Organization reporting systems for cytopathology and a brief overview of the first editions of the lung and pancreaticobiliary systems.

The International Academy of Cytology has joined with the International Agency for Research on Cancer and the World Health Organization (WHO) to develop international systems for reporting the cytopathology of lung, pancreas and biliary tract, lymph nodes, soft tissue, liver, breast, and kidney and adrenal gland. The WHO recently published the reporting systems for lung and pancreaticobiliary cytopathology. The objectives of this collaboration are to standardize the reporting of cytopathology; improve the quality of reporting by establishing the key diagnostic cytopathological features of entities and neoplasms; provide detailed best-practice guidelines in sampling techniques, specimen handling and processing, and the use of ancillary techniques; and facilitate communication between cytopathologists and clinicians to improve patient care. Each WHO system has defined specific categories and terminology for reporting cytopathology, and each category has an estimated risk of malignancy as far as the current literature allows and a suggested diagnostic management algorithm to assist clinicians. The WHO systems recognize that local medical and pathology infrastructure will vary, particularly in low-income and middle-income countries, and the WHO systems and their diagnostic management recommendations have been developed to allow them to be applied worldwide in all resource settings. The process of the selection of editors and authors and the writing and editing responsibilities has used the same model as that used for the fifth edition WHO Classification of Tumours, to which the WHO cytopathology systems are directly linked. This review provides the rationale and history of this joint International Academy of Cytology, International Agency for Research on Cancer, and WHO cytopathology project and a brief overview of the WHO reporting systems for lung and pancreaticobiliary cytopathology.

A brief review of the WHO reporting system for lung cytopathology.

The International Academy of Cytology has joined with the International Agency for Research on Cancer to bring together a group of experts in lung cytopathology to develop a WHO Reporting System for Lung Cytopathology (WHO System). This System aims to improve and standardize the reporting of cytopathology, facilitate communication between cytopathologists and clinicians, and improve patient care. The WHO System describes 5 categories for reporting lung cytopathology: 'Insufficient/Inadequate/Nondiagnostic', 'Benign', 'Atypical', 'Suspicious for malignancy', and 'Malignant', each one with a clear descriptive term, a definition, a risk of malignancy, and a suggested management algorithm. The key diagnostic cytopathologic features of each of the lesions within each category have been established by consensus through an Expert Editorial Board, who are also the authors of this review and selected for each reporting system and chosen based on their expertise in the field and/or diversity of geographical representation. Many other co-authors from around the world also contributed. The assignment of writing and editing responsibilities used the same model as that used for the WHO Classification of Tumours (https://whobluebooks.iarc.fr/about/faq/). The WHO System provides the best practice application of ancillary testing, including immunocytochemistry and molecular pathology, and guides in sampling and processing techniques to optimize the handling and preparation of specimens. The WHO System was created by the authors to be applicable globally and is based on cytomorphology with possibilities for additional diagnostic management of the patient. The authors are aware that local medical and pathology resources would differ, especially in low- and middle-income countries. The WHO Tumour Classification for Thoracic Tumors, Fifth Edition, is directly accessible through the online WHO System.

Telomere length assessment and molecular characterization of TERT gene promoter in periampullary carcinomas.

Genetic and epigenetic alterations of the telomere maintenance machinery like telomere length and telomerase reverse transcriptase (encoded by TERT gene) are reported in several human malignancies. However, there is limited knowledge on the status of the telomere machinery in periampullary carcinomas (PAC) which are rare and heterogeneous groups of cancers arising from different anatomic sites around the ampulla of Vater. In the current study, we investigated the relative telomere length (RTL) and the most frequent genetic and epigenetic alterations in the TERT promoter in PAC and compared it with tumor-adjacent nonpathological duodenum (NDu). We found shorter RTLs (1.27 vs 1.33, P = 0.01) and lower TERT protein expression (p = 0.04) in PAC tissues as compared to the NDu. Although we did not find any mutation at two reactivating hotspot mutation sites of the TERT promoter, we detected polymorphism in 45% (9/20) of the cases at rs2853669 (T > C). Also, we found a hypermethylated region in the TERT promoter of PACs consisting of four CpGs (cg10896616 with Δß 7%; cg02545192 with Δß 9%; cg03323598 with Δß 19%; and cg07285213 with Δß 15%). In conclusion, we identified shorter telomeres with DNA hypermethylation in the TERT promoter region and lower TERT protein expression in PAC tissues. These results could be used further to investigate molecular pathology and develop theranostics for PAC.

A brief review of the WHO reporting system for pancreaticobiliary cytopathology.

The World Health Organization (WHO), the International Academy of Cytology, and the International Agency for Research on Cancer have developed an approach to standardized reporting of pancreaticobiliary cytopathology. The WHO Reporting System for Pancreaticobiliary Cytopathology (WHO System) revises the Papanicolaou Society of Cytopathology (PSC) System for Reporting Pancreaticobiliary Cytology published in 2015 and replaces the 6 PSC categories with 7 categories: "Insufficient/Inadequate/Nondiagnostic"; "Benign/Negative for malignancy"; "Atypical"; "Pancreaticobiliary neoplasm, low risk/grade (PaN-low)"; "Pancreatic neoplasm, high risk/grade (PaN-High)"; "Suspicious for malignancy"; and "Malignant". In the PSC system, there is a single category for "Neoplastic" lesions that includes 2 groups, 1 for benign neoplasms and 1 named "Neoplastic-other", dominated by premalignant intraductal neoplasms primarily intraductal papillary mucinous neoplasms and low-grade malignant neoplasms (pancreatic neuroendocrine tumors (PanNET) and solid pseudopapillary neoplasms (SPN). In the WHO System, benign neoplasms with virtually no risk of malignancy are included in the "Benign" category and low-grade malignancies (PanNET and SPN) are included in the "Malignant" category, as per the 5th edition of the WHO Classification of Digestive System Tumors, while the non-invasive pre-malignant lesions of the ducts are divided by the cytomorphological grade of the epithelium into PaN-low and PaN-high with distinctly different risks of malignancy. Within each category, key diagnostic cytopathologic features and the ancillary studies for diagnostic and prognostic evaluation, as well as the implications of diagnosis for patient care and management, are outlined. Reporting and diagnostic management options recognize the variations in the availability of diagnostic and prognostic ancillary testing modalities in low- and middle-income countries.

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML.

BACKGROUND: Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS: A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS: The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS: A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.

The World Health Organization Reporting System for Pancreaticobiliary Cytopathology.

The World Health Organization (WHO), the International Academy of Cytology, and the International Agency for Research on Cancer, with expert contributors from around the world, present an international approach to standardized reporting of pancreaticobiliary cytopathology. This reporting system is one of the first in a series from various body sites that mirror the WHO Classification of Tumours series and provides an evidence-based terminology system with associated risk of malignancy and diagnostic management recommendation per diagnostic category. The WHO Reporting System for Pancreaticobiliary Cytopathology (WHO system) revises the Papanicolaou Society of Cytopathology (PSC) system for Reporting Pancreaticobiliary Cytology published in 2015 and replaces the six-tiered system with a seven-tiered system: "insufficient/inadequate/nondiagnostic"; "benign (negative for malignancy)," "atypical," "pancreaticobiliary neoplasm of low risk/low grade," "pancreatic neoplasm of high risk/high grade," "suspicious for malignancy," and "malignant." The principal differences between the WHO and the PSC systems revolve around the classification of neoplasia. In the PSC system, there was a single category for "neoplastic" lesions that includes two groups, one for "benign neoplasms" [primarily serous cystadenoma] and one named "other," dominated by premalignant intraductal neoplasms (primarily intraductal papillary mucinous neoplasms) and low-grade malignant neoplasms [pancreatic neuroendocrine tumors (PanNETs) and solid pseudopapillary neoplasms (SPNs)]. In the WHO system, benign neoplasms with virtually no risk of malignancy are included in the "benign" category and low-grade malignancies (PanNET and SPN) are included in the "malignant" category, as per the WHO Classification of Digestive System Tumours, thus leaving in the "neoplasm" category primarily those noninvasive premalignant lesions of the ductal system. These neoplasms are divided by the cytomorphological grade of the epithelium into low risk/low-grade and high risk/high-grade, with distinctly different risks of malignancy. As with the PSC system, the WHO system advocates close correlation with imaging and encourages incorporation of ancillary testing into the final diagnosis, such as biochemical (CEA and amylase) and molecular testing of cyst fluid and bile duct brushings. Key diagnostic cytopathological features of specific lesions or neoplasms, ancillary studies for diagnostic and prognostic evaluation, and implications of diagnosis for patient care and management are discussed. In addition, the WHO system includes reporting and diagnostic management options that recognize the variations in the availability of diagnostic and prognostic ancillary testing modalities in low- and middle-income countries, where cytopathology is particularly useful and is increasingly available in the absence of histopathological services.

Phyllodes tumour evidence gaps mapped from the 5th edition of the WHO classification of tumours of the breast.

AIMS: Breast phyllodes tumours (PTs) are a rare subset of fibroepithelial neoplasms categorised into benign, borderline, and malignant grades according to the World Health Organization (WHO) Classification of Tumours (WCTs). In this report, we developed an evidence gap map (EGM) based on the literature cited in the PT chapter of the 5th edition of the breast WCT in order to identify knowledge and research gaps in PT. METHODS: A framework was first established where the dimensions of the EGM were defined as categories of tumour descriptors, tumour types, and evidence levels. Citations were collected into a Microsoft Excel form and imported into EPPI-reviewer to produce the EGM. RESULTS: The EGM showed that the "Histopathology" and "Pathogenesis" sections contained the most citations, the majority being of low-level evidence. The highest number of citations considered of moderate-level evidence were found in the "Histopathology" section. There was no high-level evidence cited in this chapter. The "Localisation", "Aetiology", and "Staging" sections had the fewest citations. CONCLUSION: This EGM provides a visual representation of the cited literature in the PT chapter of the breast WCT, revealing the lack of high-level evidence citations. There is an uneven distribution of references, probably due to citation practices. Pockets of low-level evidence are highlighted, possibly related to referencing habits, lack of relevant research, or the belief that the information presented is standard accepted fact, without the need for specific citations. Future work needs to bridge evidence gaps and broaden citations beyond those in the latest WCT.

The World Health Organization Reporting System for Lung Cytopathology.

The International Academy of Cytology has joined with the International Agency for Research on Cancer (IARC) to bring together a group of experts in lung cytopathology to develop a WHO Reporting System for Lung Cytopathology (WHO System). This WHO System defines five categories for reporting lung cytopathology, that is, "Insufficient"/"Inadequate"/"Non-diagnostic," "Benign," "Atypical," "Suspicious for malignancy," and "Malignant," each with a clear descriptive term for the category, a definition, a risk of malignancy and a suggested management algorithm. The key diagnostic cytopathology features of each of the lesions within each category have been established by consensus and will be presented more fully in a subsequent IARC e-book and published hard cover book.The WHO System provides the best practice application of ancillary testing, including immunocytochemistry and molecular pathology, and provides a review to guide sampling and processing techniques to optimize the handling and preparation of the cytopathology sample emphasizing the cytomorphological differential diagnosis to aid low-resourced settings. The authors recognize that local medical and pathology resources will vary, particularly in low- and middle-income countries, and have developed the WHO System to make it applicable worldwide based on cytomorphology with options for further diagnostic management of the patient.The online WHO System provides a direct link to the WHO Tumour Classification for Thoracic Tumours 5th Edition. It will raise the profile and use of cytopathology by increasing awareness of its current role and its potential role in the era of personalized medicine based on molecular pathology utilizing "small biopsies." Ultimately, the System will improve patient care and outcomes.This System aims to improve and standardize the reporting of cytopathology, facilitate communication between cytopathologists and clinicians and improve patient care. The System is based on the current role of lung cytopathology and synthesizes the existing evidence while highlighting areas requiring further research and the future potential role of lung cytopathology.

Prostate Adenocarcinoma Grade Group 1: Rationale for Retaining a Cancer Label in the 2022 World Health Organization Classification.

We present the rationale for keeping the "cancer" label for grade group 1 (GG1) prostate cancer. Maintaining GG1 as the lowest grade outweighs the potential benefits that a benign designation may bring. Patient and surgeon education on the vital role of active surveillance for GG1 cancers and avoidance of overtreatment should be the focus rather than such a drastic change in nomenclature.

Machine learning computational tools to assist the performance of systematic reviews: A mapping review.

BACKGROUND: Within evidence-based practice (EBP), systematic reviews (SR) are considered the highest level of evidence in that they summarize the best available research and describe the progress in a determined field. Due its methodology, SR require significant time and resources to be performed; they also require repetitive steps that may introduce biases and human errors. Machine learning (ML) algorithms therefore present a promising alternative and a potential game changer to speed up and automate the SR process. This review aims to map the current availability of computational tools that use ML techniques to assist in the performance of SR, and to support authors in the selection of the right software for the performance of evidence synthesis. METHODS: The mapping review was based on comprehensive searches in electronic databases and software repositories to obtain relevant literature and records, followed by screening for eligibility based on titles, abstracts, and full text by two reviewers. The data extraction consisted of listing and extracting the name and basic characteristics of the included tools, for example a tool's applicability to the various SR stages, pricing options, open-source availability, and type of software. These tools were classified and graphically represented to facilitate the description of our findings. RESULTS: A total of 9653 studies and 585 records were obtained from the structured searches performed on selected bibliometric databases and software repositories respectively. After screening, a total of 119 descriptions from publications and records allowed us to identify 63 tools that assist the SR process using ML techniques. CONCLUSIONS: This review provides a high-quality map of currently available ML software to assist the performance of SR. ML algorithms are arguably one of the best techniques at present for the automation of SR. The most promising tools were easily accessible and included a high number of user-friendly features permitting the automation of SR and other kinds of evidence synthesis reviews.

Understanding the use of evidence in the WHO Classification of Tumours: a protocol for an evidence gap map of the classification of tumours of the lung.

INTRODUCTION: There are gaps in the evidence base of tumour classification despite being essential for cancer diagnosis, treatment and patient care. The WHO in charge of the production of an updated international classification, the WHO Classification of Tumours (WCT), aims to adapt evidence gap map (EGM) methodology to inform future editions of the WCT, by providing a visual summary of the existing evidence. METHODS AND ANALYSIS: Bibliographical references used in the WCT fifth edition of Tumours of the Lung (Thoracic Tumours volume) will be used as search results of a literature search. A descriptive analysis of the cited evidence for tumour types and descriptors will be drafted and plotted in EPPI-Reviewer to develop a visual evidence map. The resulting EGM will reflect the number of cited studies in the size of the spheres, and the level of evidence by applying a four-colour code (red=low level evidence, orange=moderate level, green=high level and blue=unclassifiable). Overview of the findings will be provided in narrative form and a report will discuss the overall stage of cited research in the WCT and will include analysis of gaps, under-researched categories of tumour descriptors and pockets of low-level evidence. ETHICS AND DISSEMINATION: No ethics approval will be required as this is a study of previously published material. Findings of the EGM will be published and used to guide editors, stakeholders and researchers for future research planning and related decision-making, especially for the development of future editions of the WCT. PROSPERO REGISTRATION NUMBER: CRD42022302327.