An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)

Recent Advances in Genitourinary Tumors: Updates From the 5th Edition of the World Health Organization Blue Book Series

CONTEXT.—

Urinary and Male Genital Tumours is the 8th volume of the World Health Organization Classification of Tumours series, 5th edition. Released in hard copy in September 2022, it presents an update to the classification of male genital and urinary tumors in the molecular age. Building upon previous volumes in this series, significant effort has been made to harmonize terminology across organ systems for biologically similar tumors (eg, neuroendocrine tumors). Genomic terminology has been standardized and genetic syndromes covered more comprehensively. This review presents a concise summary of this volume, highlighting new entities, notable modifications relative to the 4th edition, and elements of relevance to routine clinical practice.

OBJECTIVE.—

To provide a comprehensive update on the World Health Organization classification of urinary and male genital tumors, highlighting updated diagnostic criteria and terminology.

DATA SOURCES.—

The 4th and 5th editions of the World Health Organization Classification of Tumours: Urinary and Male Genital Tumours.

CONCLUSIONS.—

The World Health Organization has made several changes in the 5th edition of the update on urinary and male genital tumors that pathologists need to be aware of for up-to-date clinical practice.

Immunohistochemical Stain-Aided Annotation Accelerates Machine Learning and Deep Learning Model Development in the Pathologic Diagnosis of Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is an epithelial cancer originating in the nasopharynx epithelium. Nevertheless, annotating pathology slides remains a bottleneck in the development of AI-driven pathology models and applications. In the present study, we aim to demonstrate the feasibility of using immunohistochemistry (IHC) for annotation by non-pathologists and to develop an efficient model for distinguishing NPC without the time-consuming involvement of pathologists. For this study, we gathered NPC slides from 251 different patients, comprising hematoxylin and eosin (H&E) slides, pan-cytokeratin (Pan-CK) IHC slides, and Epstein-Barr virus-encoded small RNA (EBER) slides. The annotation of NPC regions in the H&E slides was carried out by a non-pathologist trainee who had access to corresponding Pan-CK IHC slides, both with and without EBER slides. The training process utilized ResNeXt, a deep neural network featuring a residual and inception architecture. In the validation set, NPC exhibited an AUC of 0.896, with a sensitivity of 0.919 and a specificity of 0.878. This study represents a significant breakthrough: the successful application of deep convolutional neural networks to identify NPC without the need for expert pathologist annotations. Our results underscore the potential of laboratory techniques to substantially reduce the workload of pathologists.