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Accurate terminology is the basis for clear communication among specialists and relies upon precise definitions, indispensable for the WHO Classification of Tumours. We identified a number of potentially misleading terms in use in the recently published WHO Classification of Tumours, 5th edition. From a list of common sources that might be consulted by specialists in the pathology field, we searched for definitions of the terms. Where at least two sources provided definitions for a term, we assessed their level of agreement using an ad hoc developed scale. We identified 26 potentially misleading terms from the 5th edition Digestive System and Breast Tumour Books, and 16 sources. The number of definitions provided by the sources ranged from no definition (for four terms) to ten (for two terms). No source had definitions for all terms. We found only 111 (27%) of a possible 416 definitions. Where two or more definitions were present for a term, the level of agreement between them was judged to be high. There was a paucity of definitions for potentially misleading terms in the sources consulted, but there was a good agreement when two or more definitions were present. In a globalized world where healthcare workers and learners in many fields may access these sources to learn about terminology with which they are unfamiliar, the lack of definitions is a hindrance to a precise understanding of classification in the speciality of pathology and to clear communication between specialist groups. Supplementary Information The online version contains supplementary material available at 10.1007/s00428-021-03069-7.

In preclinical studies that involve animal models for hepatic fibrosis, accurate quantification of the fibrosis is of utmost importance. The use of digital image analysis based on deep learning artificial intelligence (AI) algorithms can facilitate accurate evaluation of liver fibrosis in these models. In the present study, we compared the quantitative evaluation of collagen proportionate area in the carbon tetrachloride model of liver fibrosis in the mouse by a newly developed AI algorithm to the semiquantitative assessment of liver fibrosis performed by a board-certified toxicologic pathologist. We found an excellent correlation between the 2 methods of assessment, most evident in the higher magnification (×40) as compared to the lower magnification (×10). These findings strengthen the confidence of using digital tools in the toxicologic pathology field as an adjunct to an expert toxicologic pathologist.

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

Many technological advances have been made in the recent years, several of them with a great potential of significantly improving the diagnostic pathology field. This article discusses three of the most promising technologies, which emerged in the last one year. Fluorescent in situ sequencing can lead to the simultaneous identification of the transcriptome-wide RNA in individual cells across a tissue sections. 3D microscopy together with advanced image analysis can be used in diagnostic pathology and will especially be useful in hard to diagnose cases where the spatial relationship of the tissue components is important. Expansion microscopy physically expands the biological specimen, and is of great interest for diagnostic pathology since the cheap conventional microscopes can be used to image a symmetrically expanded tissue. In addition, digital analysis and computational pathology are an integral part of each of these three emerging technologies, which underline their importance for the future developments in diagnostic pathology.

Quantitative image analysis (IA) is a rapidly evolving area of digital pathology. Although not a new concept, the quantification of histological features on photomicrographs used to be cumbersome, resource-intensive, and limited to specialists and specialized laboratories. Recent technological advances like highly efficient automated whole slide digitizer (scanner) systems, innovative IA platforms, and the emergence of pathologist-friendly image annotation and analysis systems mean that quantification of features on histological digital images will become increasingly prominent in pathologists’ daily professional lives. The added value of quantitative IA in pathology includes confirmation of equivocal findings noted by a pathologist, increasing the sensitivity of feature detection, quantification of signal intensity, and improving efficiency. There is no denying that quantitative IA is part of the future of pathology; however, there are also several potential pitfalls when trying to estimate volumetric features from limited 2-dimensional sections. This continuing education session on quantitative IA offered a broad overview of the field; a hands-on toxicologic pathologist experience with IA principles, tools, and workflows; a discussion on how to apply basic stereology principles in order to minimize bias in IA; and finally, a reflection on the future of IA in the toxicologic pathology field.

When considering the paucity of males in the speech-language pathology field and the continued interest to increase the diversity within the profession, the current study seeks to identify variables that may deter male students from choosing communication sciences and disorders as a major. Survey responses from 290 males from two universities were analyzed. Findings indicated male undergraduate students have minimal awareness of the speech-language-pathology profession. This was seen in overall knowledge as well as the scope of the practice. Overall, despite the limited awareness and knowledge of the profession, the students' perceptions were positive towards the profession (e.g. intellectually challenging and requires independent thinking). Further reported perceptions and implications for future research are discussed.

Tumor biopsies are often Formalin-Fixed and Paraffin-Embedded (FFPE) for histological staining, genetic testing and archival purposes. Formalin treatment preserves tissue by crosslinking proteins, but can lead to mutation of the nucleic acid bases that can be detected by next-generation sequencing (NGS) methods despite being unrelated to the cancer. Studies have shown that certain fixation protocols are compatible with high quality nucleic acid isolation and variant calling by NGS. These studies used tissue fixed with 10% neutral buffered formalin for 24 hours, a standard protocol in the pathology field. In practice, however, FFPE sample handling varies from site-to-site. As we processed FFPE samples from various sites, we found that the quality of the isolated nucleic acids and subsequent sequencing results varied substantially. We hypothesized that this is due to deviations from the standard protocol such as improperly buffered reagents, variation in the fixation time and temperature, microwaving, and varied storage conditions of the samples. To understand the role of formalin fixation on the quality of the isolated nucleic acids and subsequent NGS analyses, we subjected the widely studied cell line NA12878 to various formalin fixation conditions. We then performed an augmented target enrichment and sequencing assay on both the DNA and RNA isolated from fresh frozen (FF) and formalin fixed (FFPE) NA12878 samples. We assessed raw nucleic acid quality, library quality, alignment rate, duplication rate, on-target efficiency and variant concordance between FF and FFPE. We noted substantial negative effects on sequencing library quality associated with sample incubation in unbuffered formalin, at high temperatures, and long periods of time (e.g. 3 days rather than 1 day). These harshly treated samples also showed poor alignment qualities, higher duplication rates, and lower mapping qualities. At the small variant level, they showed an increase in global C>T deamination (CG->TA, 50% FFPE vs 35% in FF) and oxidation (CG->AT, 15% FFPE vs 10% FF) rates. Variants caused by formalin fixation were most commonly detected at low ( Using the results of this study, we intend to provide guidance on optimal fixation conditions for NGS applications. It is crucial to fix tissues in buffered formalin for less than 24hr at room temperature to preserve nucleic acid integrity and amenability for downstream NGS assays. We also demonstrate how a deeper understanding of the effects of formalin can improve variant calling from FFPE tissues, especially at lower AFs where formalin-related errors have the greatest impact. Citation Format: Ravi Alla, Shujun Luo, Elena Helman, Sean M. Boyle, Michael J. Clark, Kirk Scott, Parin Sripakdeevong, Mirian Karbelashvili, Deanna M. Church, Michael Snyder, John West, Richard Chen. Fix the fixation: effect of formalin fixation on targeted sequencing, variant calling and gene expression. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3616.