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Digital pathology, Whole slide images, Informatics, Breast, Ex vivo microscopy, Gross imaging, Computational pathology Keywords: Digital pathology; Whole slide images; Informatics; Breast; Ex vivo microscopy; Gross imaging; Computational pathology EN Digital pathology Whole slide images Informatics Breast Ex vivo microscopy Gross imaging Computational pathology 209 210 2 03/15/23 20230301 NES 230301 Digital pathology has been accelerating, with advances in computational pathology that move beyond traditional H&E and immunohistochemistry (IHC) images.[1],[2] Digital gross imaging has long been in routine use[3] but seems overdue for disruption, especially with novel imaging techniques such as those in the article by Zhang et al[4] in this issue of I AJCP i . [Extracted from the article]

Objectives Our aim was to explore the performance of TRPS1 as an immunohistochemical diagnostic marker; find the optimal conditions for its use in breast carcinomas, especially triple-negative breast cancers (TNBCs); and compare its results in carcinomas of a select few organ sites, with an emphasis on gynecologic tumors. Methods Tissue microarrays from breast carcinomas (n = 197), endometrial adenocarcinomas (n = 69), ovarian tumors (n = 250), vulvar squamous cell carcinomas (n = 97), pancreatic ductal adenocarcinomas (n = 20), and gastric adenocarcinomas (n = 12) were stained with TRPS1 using 2 different conditions (protocol 1: high pH; protocol 2: low pH). Breast carcinomas consisted of hormone receptor (HR)–positive/ERBB2 (formerly HER2 or HER2/neu)–negative (n = 53) samples, HR-positive/ERBB2-positive (n = 6) samples, and TNBCs (n = 138). Results Comparing TRPS1 results in breast carcinomas vs tumors from other organ sites, the sensitivity of TRPS1 was 91% and 87%, respectively, while the specificity was 66% and 74% for protocol 1 and 2, respectively. For TNBCs vs gynecologic tumors, the sensitivity of TRPS1 was 89% and 85%, respectively, while the specificity was 65% and 73%, respectively. Conclusions TRPS1 stains approximately 90% of breast carcinomas but also up to 71% of endometrial carcinomas, albeit with a weaker median expression. Our data show that although TRPS1 is a highly sensitive marker for TNBCs, it is not as highly specific as previously reported. [ABSTRACT FROM AUTHOR]

The author focuses on the three-dimensional microscopic techniques such as Optical coherence tomography (OCT) that can be advantageous for diagnostic purposes. He discusses the potential benefit of creating digital images with high-resolution for histopathological studies. Also stated, that these techniques will render old techniques such as photomicrography and specimens testing for postpartum tubal ligation.

Objectives: SOX10 expression helps identify melanocytic lesions. Over time, novel uses have been identified, such as expression in triple-negative breast cancer (TNBC). We evaluated the usefulness of SOX10 in breast pathology-specifically, identification and subtyping of TNBC and distinction from gynecologic carcinomas, use as a myoepithelial marker, and in the distinction of usual ductal hyperplasia (UDH) from atypical ductal hyperplasia (ADH).Methods: Several breast and gynecologic carcinoma tissue microarrays containing a total of 492 cases were stained with SOX10. Whole sections of 34 ADH, 50 UDH, and 29 ductal carcinoma in situ (DCIS) samples were also stained with SOX10.Results: SOX10 expression was identified in 67% of consecutive TNBC cases. Expression was mostly seen in nonapocrine, androgen receptor (AR)-negative TNBCs. All gynecologic carcinomas (n = 157) were negative. All UDH cases showed mosaic SOX10 expression, while all ADH cases lacked expression. All estrogen receptor (ER)-positive DCIS (n = 19) specimens were negative for SOX10, while 2 of 10 ER-negative DCIS specimens were positive for SOX10. The latter 2 cases showed SOX10-positive invasive carcinomas.Conclusions: SOX10 identifies nonluminal AR-type TNBC and is useful in distinguishing TNBC from gynecologic carcinomas. SOX10 can distinguish UDH from ADH. SOX10 is not useful in distinguishing ADH from DCIS. [ABSTRACT FROM AUTHOR]

Segmenting a broad class of histological structures in transmitted light and/or fluorescence-based images is a prerequisite for determining the pathological basis of cancer, elucidating spatial interactions between histological structures in tumor microenvironments (e.g., tumor infiltrating lymphocytes), facilitating precision medicine studies with deep molecular profiling, and providing an exploratory tool for pathologists. This paper focuses on segmenting histological structures in hematoxylin- and eosin-stained images of breast tissues, e.g., invasive carcinoma, carcinoma in situ, atypical and normal ducts, adipose tissue, and lymphocytes. We propose two graph-theoretic segmentation methods based on local spatial color and nuclei neighborhood statistics. For benchmarking, we curated a data set of 232 high-power field breast tissue images together with expertly annotated ground truth. To accurately model the preference for histological structures (ducts, vessels, tumor nets, adipose, etc.) over the remaining connective tissue and non-tissue areas in ground truth annotations, we propose a new region-based score for evaluating segmentation algorithms. We demonstrate the improvement of our proposed methods over the state-of-the-art algorithms in both region- and boundary-based performance measures. [ABSTRACT FROM PUBLISHER]

Objectives: Phyllodes tumors (PTs) are categorized by the World Health Organization (WHO) as benign, borderline, and malignant. Singapore General Hospital (SGH) nomogram is a recurrence risk assessment tool for PT, which uses cytologic atypia, mitosis, stromal overgrowth, and the surgical margin status. We studied the prognostic significance of WHO classification and its correlation to the SGH nomogram.Methods: We identified 270 consecutive cases of PT (195 benign, 49 borderline, 26 malignant). Follow-up was available on 246 cases (mean follow-up of 51 months).Results: The recurrence rates were 2% (4 of 176) for benign, 4% (2 of 46) for borderline, and 25% (6 of 24) for malignant (log-rank test P < .0001 for recurrence-free survival). Only five patients with malignant PT experienced distant recurrence. Stromal overgrowth was an independent predictor of recurrence-free survival on multivariable analysis. The mean nomogram scores for benign, borderline, and malignant PT were 20, 20.3, and 32, respectively. The higher than expected score for benign PT was due to positive margins in 39% of cases.Conclusions: The WHO three-tiered classification of PT is prognostic. Despite positive margin status, most benign PTs do not recur. Other features of the nomogram help in determining recurrence but are also used for WHO classification. [ABSTRACT FROM AUTHOR]

Context.--Pathologists' computer-assisted diagnosis (pCAD) is a proposed framework for alleviating challenges through the automation of their routine sign-out work. Currently, hypothetical pCAD is based on a triad of advanced image analysis, deep integration with heterogeneous information systems, and a concrete understanding of traditional pathology workflow. Prototyping is an established method for designing complex new computer systems such as pCAD. Objective.--To describe, in detail, a prototype of pCAD for the sign-out of a breast cancer specimen. Design.--Deidentified glass slides and data from breast cancer specimens were used. Slides were digitized into whole-slide images with an Aperio ScanScope XT, and screen captures were created by using vendor-provided software. The advanced workflow prototype was constructed by using PowerPoint software. Results.--We modeled an interactive, computer-assisted workflow: pCAD previews whole-slide images in the context of integrated, disparate data and predefined diagnostic tasks and subtasks. Relevant regions of interest (ROIs) would be automatically identified and triaged by the computer. A pathologist's sign-out work would consist of an interactive review of important ROIs, driven by required diagnostic tasks. The interactive session would generate a pathology report automatically. Conclusions.--Using animations and real ROIs, the pCAD prototype demonstrates the hypothetical sign-out in a stepwise fashion, illustrating various interactions and explaining how steps can be automated. The file is publicly available and should be widely compatible. This mock-up is intended to spur discussion and to help usher in the next era of digitization for pathologists by providing desperately needed and long-awaited automation. [ABSTRACT FROM AUTHOR]

Background: Digital slides obtained from whole slide imaging (WSI) platforms are typically viewed in two dimensions using desktop personal computer monitors or more recently on mobile devices. To the best of our knowledge, we are not aware of any studies viewing digital pathology slides in a virtual reality (VR) environment. VR technology enables users to be artificially immersed in and interact with a computer-simulated world. Oculus Rift is among the world's first consumer-targeted VR headsets, intended primarily for enhanced gaming. Our aim was to explore the use of the Oculus Rift for examining digital pathology slides in a VR environment. Methods: An Oculus Rift Development Kit 2 (DK2) was connected to a 64-bit computer running Virtual Desktop software. Glass slides from twenty randomly selected lymph node cases (ten with benign and ten malignant diagnoses) were digitized using a WSI scanner. Three pathologists reviewed these digital slides on a 27-inch 5K display and with the Oculus Rift after a 2-week washout period. Recorded endpoints included concordance of final diagnoses and time required to examine slides. The pathologists also rated their ease of navigation, image quality, and diagnostic confidence for both modalities. Results: There was 90% diagnostic concordance when reviewing WSI using a 5K display and Oculus Rift. The time required to examine digital pathology slides on the 5K display averaged 39 s (range 10–120 s), compared to 62 s with the Oculus Rift (range 15–270 s). All pathologists confirmed that digital pathology slides were easily viewable in a VR environment. The ratings for image quality and diagnostic confidence were higher when using the 5K display. Conclusion: Using the Oculus Rift DK2 to view and navigate pathology whole slide images in a virtual environment is feasible for diagnostic purposes. However, image resolution using the Oculus Rift device was limited. Interactive VR technologies such as the Oculus Rift are novel tools that may be of use in digital pathology. [ABSTRACT FROM AUTHOR]

Many pathology laboratories are implementing digital pathology systems. The image resolution and scanning (digitization) magnification can vary greatly between these digital pathology systems. In addition, when digital images are compared with viewing images using a microscope, the cellular features can vary in size. This article highlights differences in magnification and resolution between the conventional microscopes and the digital pathology systems. As more pathologists adopt digital pathology, it is important that they understand these differences and how they ultimately translate into what the pathologist can see and how this may impact their overall viewing experience. [ABSTRACT FROM AUTHOR]