Moving Forward on Tumor Pathology Research Reporting: A Guide for Pathologists From the World Health Organization Classification of Tumors Living Evidence Gap Map by Tumour Type Group.

Evidence-based medicine (EBM) can be an 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, reviewed and endorsed by the Word Health Organization International Agency for Research on Cancer's International Collaboration for Cancer Classification and Research, we aim to help pathologists and researchers 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, a framework for research questions, and 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 hence, here, we collate the key reporting data set 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 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.

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.

Eating disorders and the risk of developing cancer: a systematic review.

PURPOSE: Evidence concerning eating disorders as risk toward developing cancer is sparse. Energy restriction might be cancer protective, while malnutrition, vomiting, laxative and substance use might stimulate cancer development. We examined whether individuals with an eating disorder (not restricted to anorexia nervosa) had a different risk of developing cancer. METHODS: A systematic search on Medline and Embase until 28th April 2020 identified relevant human original research publications, including all populations and all cancer types. RESULTS: From 990 records, 6 case reports and 9 cohorts were included. Some cohorts found a decreased breast (3/5 studies) or cervical (1/2) cancer risk, while an increased esophageal (2/3), liver (1/1), brain (1/1 in men) and respiratory (2/4) cancer risk, but other cancer risks were non-significant, and an increased mortality overall (1/2), from breast (1/1), female genital (1/1) and skin (1/1) cancer in eating disorder patients. The case reports further described esophageal cancer and leukemia. No clear statistical differences in cancer risk were found depending on eating disorder type, perhaps due to the small sample size (n = 1783 for other than anorexia nervosa). CONCLUSIONS: The literature on eating disorders and cancer risk is sparse with many gaps. Hormonal changes, sexual activity, nutritional status, vomiting and concomitant tobacco/alcohol abuse may explain increased/decreased cancer risk. Future large studies (now 1-366 cancer cases) that also include men (now 4.7%), bulimia nervosa (now 3.8%) and several cancer sites (now mainly breast cancer) are needed and should foresee longer follow-up time (now 5.4-15.2 years) and extensive confounder adjustment (now only age and sex). LEVEL OF EVIDENCE: Level I, systematic review.

The Human Microbiome in Relation to Cancer Risk: A Systematic Review of Epidemiologic Studies.

The microbiome has been hypothesized to play a role in cancer development. Because of the diversity of published data, an overview of available epidemiologic evidence linking the microbiome with cancer is now needed. We conducted a systematic review using a tailored search strategy in Medline and EMBASE databases to identify and summarize the current epidemiologic literature on the relationship between the microbiome and different cancer outcomes published until December 2019. We identified 124 eligible articles. The large diversity of parameters used to describe microbial composition made it impossible to harmonize the different studies in a way that would allow meta-analysis, therefore only a qualitative description of results could be performed. Fifty studies reported differences in the gut microbiome between patients with colorectal cancer and various control groups. The most consistent findings were for Fusobacterium, Porphyromonas, and Peptostreptococcus being significantly enriched in fecal and mucosal samples from patients with colorectal cancer. For the oral microbiome, significantly increased and decreased abundance was reported for Fusobacterium and Streptococcus, respectively, in patients with oral cancer compared with controls. Overall, although there was a large amount of evidence for some of these alterations, most require validation in high-quality, preferably prospective, epidemiologic studies.