The Importance of eSlide Macro Images for Primary Diagnosis with Whole Slide Imaging

Revealing intact neuronal circuitry in centimeter-sized formalin-fixed paraffin-embedded brain

Tissue-clearing and labeling techniques have revolutionized brain-wide imaging and analysis, yet their application to clinical formalin-fixed paraffin-embedded (FFPE) blocks remains challenging. We introduce HIF-Clear, a novel method for efficiently clearing and labeling centimeter-thick FFPE specimens using elevated temperature and concentrated detergents. HIF-Clear with multi-round immunolabeling reveals neuron circuitry regulating multiple neurotransmitter systems in a whole FFPE mouse brain and is able to be used as the evaluation of disease treatment efficiency. HIF-Clear also supports expansion microscopy and can be performed on a non-sectioned 15-year-old FFPE specimen, as well as a 3-month formalin-fixed mouse brain. Thus, HIF-Clear represents a feasible approach for researching archived FFPE specimens for future neuroscientific and 3D neuropathological analyses.

Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives

Background. Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.

Quality Management System in Clinical Digital Pathology Operations at a Tertiary Cancer Center

Digital pathology workflows can improve pathology operations by allowing reliable and fast retrieval of digital images, digitally reviewing pathology slides, enabling remote work and telepathology, use of computer-aided tools, and sharing of digital images for research and educational purposes. The need for quality systems is a prerequisite for successful clinical-grade digital pathology adoption and patient safety. In this article, we describe the development of a structured digital pathology laboratory quality management system (QMS) for clinical digital pathology operations at Memorial Sloan Kettering Cancer Center (MSK). This digital pathology-specific QMS development stemmed from the gaps that were identified when MSK integrated digital pathology into its clinical practice. The digital scan team in conjunction with the Department of Pathology and Laboratory Medicine quality team developed a QMS tailored to the scanning operation to support departmental and institutional needs. As a first step, systemic mapping of the digital pathology operations identified the prescan, scan, and postscan processes; instrumentation; and staffing involved in the digital pathology operation. Next, gaps identified in quality control and quality assurance measures led to the development of standard operating procedures and training material for the different roles and workflows in the process. All digital pathology-related documents were subject to regulatory review and approval by departmental leadership. The quality essentials were developed into an extensive Digital Pathology Quality Essentials framework to specifically address the needs of the growing clinical use of digital pathology technologies. Using the unique digital experience gained at MSK, we present our recommendations for QMS for large-scale digital pathology operations in clinical settings.

Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives

In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods to analyze digital histopathological images, based on artificial intelligence techniques and specific models, quantify the required information with significantly higher consistency and precision compared to that provided by optical microscopy. In parallel, the unprecedented advances in machine learning facilitate, through the synergy of artificial intelligence and digital pathology, the possibility of diagnosis based on image analysis, previously limited only to certain specialties. Therefore, the integration of digital images into the study of pathology, combined with advanced algorithms and computer-assisted diagnostic techniques, extends the boundaries of the pathologist's vision beyond the microscopic image and allows the specialist to use and integrate his knowledge and experience adequately. We conducted a search in PubMed on the topic of digital pathology and its applications, to quantify the current state of knowledge. We found that computer-aided image analysis has a superior potential to identify, extract and quantify features in more detail compared to the human pathologist's evaluating possibilities; it performs tasks that exceed its manual capacity, and can produce new diagnostic algorithms and prediction models applicable in translational research that are able to identify new characteristics of diseases based on changes at the cellular and molecular level.

MIST: multiple instance learning network based on Swin Transformer for whole slide image classification of colorectal adenomas

Colorectal adenoma is a recognized precancerous lesion of colorectal cancer (CRC), and at least 80% of colorectal cancers are malignantly transformed from it. Therefore, it is essential to distinguish benign from malignant adenomas in the early screening of colorectal cancer. Many deep learning computational pathology studies based on whole slide images (WSIs) have been proposed. Most approaches require manual annotation of lesion regions on WSIs, which is time-consuming and labor-intensive. This study proposes a new approach, MIST - Multiple Instance learning network based on the Swin Transformer, which can accurately classify colorectal adenoma WSIs only with slide-level labels. MIST uses the Swin Transformer as the backbone to extract features of images through self-supervised contrastive learning and uses a dual-stream multiple instance learning network to predict the class of slides. We trained and validated MIST on 666 WSIs collected from 480 colorectal adenoma patients in the Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University Medical School. These slides contained six common types of colorectal adenomas. The accuracy of external validation on 273 newly collected WSIs from Nanjing First Hospital was 0.784, which was superior to the existing methods and reached a level comparable to that of the local pathologist's accuracy of 0.806. Finally, we analyzed the interpretability of MIST and observed that the lesion areas of interest in MIST were generally consistent with those of interest to local pathologists. In conclusion, MIST is a low-burden, interpretable, and effective approach that can be used in colorectal cancer screening and may lead to a potential reduction in the mortality of CRC patients by assisting clinicians in the decision-making process. © 2022 The Pathological Society of Great Britain and Ireland.

Protocol for the Systematic Fixation, Circuit-Based Sampling, and Qualitative and Quantitative Neuropathological Analysis of Human Brain Tissue

Human brain tissue has long been a critical resource for neuroanatomy and neuropathology, but with the advent of advanced imaging and molecular sequencing techniques, it has become possible to use human brain tissue to study, in great detail, the structural, molecular, and even functional underpinnings of human brain disease. In the century following the first description of Alzheimer's disease (AD), numerous technological advances applied to human tissue have enabled novel diagnostic approaches using diverse physical and molecular biomarkers, and many drug therapies have been tested in clinical trials (Schachter and Davis, Dialogues Clin Neurosci 2:91-100, 2000). The methods for brain procurement and tissue stabilization have remained somewhat consistently focused on formalin fixation and freezing. Although these methods have enabled research protocols of multiple modalities, new, more advanced technologies demand improved methodologies for the procurement, characterization, stabilization, and preparation of both normal and diseased human brain tissues. Here, we describe our current protocols for the procurement and characterization of fixed brain tissue, to enable systematic and precisely targeted diagnoses, and describe the novel, quantitative molecular, and neuroanatomical studies that broadly expand the use of formalin-fixed, paraffin-embedded (FFPE) tissue that will further our understanding of the mechanisms underlying human neuropathologies.

Development of quality assurance program for digital pathology by the Korean Society of Pathologists

Background: Digital pathology (DP) using whole slide imaging is a recently emerging game changer technology that can fundamentally change the way of working in pathology. The Digital Pathology Study Group (DPSG) of the Korean Society of Pathologists (KSP) published a consensus report on the recommendations for pathologic practice using DP. Accordingly, the need for the development and implementation of a quality assurance program (QAP) for DP has been raised.Methods: To provide a standard baseline reference for internal and external QAP for DP, the members of the Committee of Quality Assurance of the KSP developed a checklist for the Redbook and a QAP trial for DP based on the prior DPSG consensus report. Four leading institutes participated in the QAP trial in the first year, and we gathered feedback from these institutes afterwards.Results: The newly developed checklists of QAP for DP contain 39 items (216 score): eight items for quality control of DP systems; three for DP personnel; nine for hardware and software requirements for DP systems; 15 for validation, operation, and management of DP systems; and four for data security and personal information protection. Most participants in the QAP trial replied that continuous education on unfamiliar terminology and more practical experience is demanding.Conclusions: The QAP for DP is essential for the safe implementation of DP in pathologic practice. Each laboratory should prepare an institutional QAP according to this checklist, and consecutive revision of the checklist with feedback from the QAP trial for DP needs to follow.

Automated Nuclear Segmentation in Head and Neck Squamous Cell Carcinoma Pathology Reveals Relationships between Cytometric Features and ESTIMATE Stromal and Immune Scores

The tumor microenvironment (TME) plays an important role in the progression of head and neck squamous cell carcinoma (HNSCC). Currently, pathologic assessment of TME is nonstandardized and subject to observer bias. Genome-wide transcriptomic approaches to understanding the TME, while less subject to bias, are expensive and not currently a part of the standard of care for HNSCC. To identify pathology-based biomarkers that correlate with genomic and transcriptomic signatures of TME in HNSCC, cytometric feature maps were generated in a publicly available data set from a cohort of patients with HNSCC, including whole-slide tissue images and genomic and transcriptomic phenotyping (N = 49). Cytometric feature maps were generated based on whole-slide nuclear detection, using a deep-learning algorithm trained for StarDist nuclear segmentation. Cytometric features in each patient were compared to transcriptomic measurements, including Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) scores and stemness scores. With correction for multiple comparisons, one feature (nuclear circularity) demonstrated a significant linear correlation with ESTIMATE stromal score. Two features (nuclear maximum and minimum diameter) correlated significantly with ESTIMATE immune score. Three features (nuclear solidity, nuclear minimum diameter, and nuclear circularity) correlated significantly with transcriptomic stemness score. This study provides preliminary evidence that observer-independent, automated tissue-slide analysis can provide insights into the HNSCC TME which correlate with genomic and transcriptomic assessments.

Quality Control in Digital Pathology: Automatic Fragment Detection and Counting

Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, this step is still performed manually, resulting in lost time and delays in making the complete case available for evaluation by the pathologist. To overcome this limitation, we developed an autonomous system that can detect and count the number of fragments contained on each slide. We applied and compared two different methods: conventional machine learning methods and deep convolutional network methods. For conventional machine learning methods, we tested a two-stage approach with a supervised classifier followed by unsupervised hierarchical clustering. In addition, Fast R-CNN and YOLOv5, two state-of-the-art deep learning models for detection, were used and compared. All experiments were performed on a dataset comprising 1276 images of colorec-tal biopsy and polypectomy specimens manually labeled for fragment/set detection. The best results were obtained with the YOLOv5 architecture with a map@0.5 of 0.977 for fragment/set detection.

Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology

CONTEXT.—

The original guideline, "Validating Whole Slide Imaging for Diagnostic Purposes in Pathology," was published in 2013 and included 12 guideline statements. The College of American Pathologists convened an expert panel to update the guideline following standards established by the National Academies of Medicine for developing trustworthy clinical practice guidelines.

OBJECTIVE.—

To assess evidence published since the release of the original guideline and provide updated recommendations for validating whole slide imaging (WSI) systems used for diagnostic purposes.

DESIGN.—

An expert panel performed a systematic review of the literature. Frozen sections, anatomic pathology specimens (biopsies, curettings, and resections), and hematopathology cases were included. Cytology cases were excluded. Using the Grading of Recommendations Assessment, Development, and Evaluation approach, the panel reassessed and updated the original guideline recommendations.

RESULTS.—

Three strong recommendations and 9 good practice statements are offered to assist laboratories with validating WSI digital pathology systems.

CONCLUSIONS.—

Systematic review of literature following release of the 2013 guideline reaffirms the use of a validation set of at least 60 cases, establishing intraobserver diagnostic concordance between WSI and glass slides and the use of a 2-week washout period between modalities. Although all discordances between WSI and glass slide diagnoses discovered during validation need to be reconciled, laboratories should be particularly concerned if their overall WSI-glass slide concordance is less than 95%.

Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice

Using digitalized whole slide images (WSI) in routine histopathology practice is a revolutionary technology. This study aims to assess the clinical impacts of WSI quality and representation of the corresponding glass slides. 40,160 breast WSIs were examined and compared with their corresponding glass slides. The presence, frequency, location, tissue type, and the clinical impacts of missing tissue were assessed. Scanning time, type of the specimens, time to WSIs implementation, and quality control (QC) measures were also considered. The frequency of missing tissue ranged from 2% to 19%. The area size of the missed tissue ranged from 1-70%. In most cases (>75%), the missing tissue area size was <10% and peripherally located. In all cases the missed tissue was fat with or without small entrapped normal breast parenchyma. No missing tissue was identified in WSIs of the core biopsy specimens. QC measures improved images quality and reduced WSI failure rates by seven-fold. A negative linear correlation between the frequency of missing tissue and both the scanning time and the image file size was observed (p < 0.05). None of the WSI with missing tissues resulted in a change in the final diagnosis. Missing tissue on breast WSI is observed but with variable frequency and little diagnostic consequence. Balancing between WSI quality and scanning time/image file size should be considered and pathology laboratories should undertake their own assessments of risk and provide the relevant mitigations with the appropriate level of caution.

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP)

The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.

What is Essential is (No More) Invisible to the Eyes: The Introduction of BlocDoc in the Digital Pathology Workflow

BACKGROUND

The implementation of a fully digital workflow in any anatomic pathology department requires a complete conversion to a tracked system. Ensuring the strict correspondence of the material submitted for the analysis, from the accessioning to the reporting phase, is mandatory in the anatomic pathology laboratory, especially when implementing the digital pathology for primary histological diagnosis. The proposed solutions, up to now, rely on the verification that all the materials present in the glass slide are also present in the whole slide images (WSIs). Although different methods have already been implemented for this purpose (e.g., the "macroimage" of the digital slide, representing the overview of the glass slide), the recent introduction of a device to capture the cut surface of paraffin blocks put the quality control of the digital workflow a step forward, allowing to match the digitized slide with the corresponding block. This system may represent a reliable, easy-to-use alternative to further reduce tissue inconsistencies between material sent to the lab and the final glass slides or WSIs.

METHODS

The Anatomic Pathology of the Gravina Hospital in Caltagirone, Sicily, Italy, has implemented the application of the BlocDoc devices (SPOT Imaging, Sterling Heights, USA) in its digital workflow. The instruments were positioned next to every microtome/sectioning station, with the possibility to capture the "normal" and the polarized image of the cut surface of the blocks directly by the technician. The presence of a monitor in the BlocDoc device allowed the technician to check the concordance between the cut surface of the block and the material on the corresponding slide. The link between BlocDoc and the laboratory information system, through the presence of the 2D barcode, allowed the pathologists to access the captured image of the cut surface of the block at the pathologist workstation, thus enabling the direct comparison between this image and the WSI (thumbnail and "macroimage").

RESULTS

During the implementation period, more than 10.000 (11.248) blocks were routinely captured using the BlocDoc. The employment of this approach allowed a drastic reduction of the discordances and tissue inconsistencies. The implementation of the BlocDoc in the routine allowed the detection of two different types of "errors," the so-called "systematic" and "occasional" ones. The first type was intrinsic of some specific specimens (e.g., transurethral resection of the prostate, nasal polypectomies, and piecemeal uterine myomectomies) characterized by the three-dimensional nature of the fragments and affected almost 100% of these samples. On the other hand, the "occasional" errors, mainly due to inexperience or extreme caution of the technicians in handling tiny specimens, affected 98 blocks (0.9%) of these samples and progressively reduced with the rising confidence with the BlocDoc. One of these cases was clinically relevant. No problems in the recognition of the 2D barcodes were encountered using a laser cassette printer. Finally, rare failures have been recorded during the period, accounting for <0.1% of all the cases, mainly due to network connection issues.

CONCLUSIONS

The implementation of BlocDoc can further improve the effectiveness of the digital workflow, demonstrating its safety and robustness as a valid alternative to the traditional, nontracked analogic workflow.

Rapid Validation of Whole-Slide Imaging for Primary Histopathology Diagnosis

OBJECTIVES

The ongoing global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitates adaptations in the practice of surgical pathology at scale. Primary diagnosis by whole-slide imaging (WSI) is a key component that would aid departments in providing uninterrupted histopathology diagnosis and maintaining revenue streams from disruption. We sought to perform rapid validation of the use of WSI in primary diagnosis meeting recommendations of the College of American Pathologists guidelines.

METHODS

Glass slides from clinically reported cases from 5 participating pathologists with a preset washout period were digitally scanned and reviewed in settings identical to typical reporting. Cases were classified as concordant or with minor or major disagreement with the original diagnosis. Randomized subsampling was performed, and mean concordance rates were calculated.

RESULTS

In total, 171 cases were included and distributed equally among participants. For the group as a whole, the mean concordance rate in sampled cases (n = 90) was 83.6% counting all discrepancies and 94.6% counting only major disagreements. The mean pathologist concordance rate in sampled cases (n = 18) ranged from 90.49% to 97%.

CONCLUSIONS

We describe a novel double-blinded method for rapid validation of WSI for primary diagnosis. Our findings highlight the occurrence of a range of diagnostic reproducibility when deploying digital methods.

Loss of fidelity in scanned digital images compared to glass slides of brain tumors resected using cavitron ultrasonic surgical aspirator

Conversion of glass slides to digital images is necessary to capitalize on advances in computational pathology and could potentially transform our approach to primary diagnosis, research, and medical education. Most slide scanners have a limited maximum scannable area and utilize proprietary tissue detection algorithms to selectively scan regions that contain tissue, allowing for increased scanning speed and reduced file size compared to scanning the entire slide at high resolution. However, very small and faintly stained tissue fragments may not be recognized by these algorithms, leading to loss of fidelity in the digital image compared to the glass slides. Cavitron ultrasonic surgical aspirator (CUSA) is frequently used in brain tumor resections, resulting in highly fragmented specimens that are used for primary diagnosis. Here we evaluated the rate of loss of fidelity in 296 digital images from 40 CUSA-resected brain tumors scanned using a Philips Ultra Fast Scanner. Overall, 54% of the slides (at least one from every case) showed loss of fidelity, with at least one tissue fragment not scanned at high resolution. The majority of the missed tissue fragments were small (<0.5 mm), but rare slides were missing fragments greater than 5 mm in greatest dimension. In addition, 19% of the slides with missing tissue showed no indication of loss of fidelity in the digital image itself; the missing tissue could only be appreciated upon review of the glass slides. These results highlight a potential liability in the use of digital images for primary diagnosis in CUSA-resected brain tumor specimens.

Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. In coping with it, histopathology image analysis (HIA) provides key information for clinical diagnosis of CRC. Nowadays, the deep learning methods are widely used in improving cancer classification and localization of tumor-regions in HIA. However, these efforts are both time-consuming and labor-intensive due to the manual annotation of tumor-regions in the whole slide images (WSIs). Furthermore, classical deep learning methods to analyze thousands of patches extracted from WSIs may cause loss of integrated information of image. Herein, a novel method was developed, which used only global labels to achieve WSI classification and localization of carcinoma by combining features from different magnifications of WSIs. The model was trained and tested using 1346 colorectal cancer WSIs from the Cancer Genome Atlas (TCGA). Our method classified colorectal cancer with an accuracy of 94.6 %, which slightly outperforms most of the existing methods. Its cancerous-location probability maps were in good agreement with annotations from three individual expert pathologists. Independent tests on 50 newly-collected colorectal cancer WSIs from hospitals produced 92.0 % accuracy and cancerous-location probability maps were in good agreement with the three pathologists. The results thereby demonstrated that the method sufficiently achieved WSI classification and localization utilizing only global labels. This weakly supervised deep learning method is effective in time and cost, as it delivered a better performance in comparison with the state-of-the-art methods.

Implementation of Collodion Bag Protocol to Improve Whole-slide Imaging of Scant Gynecologic Curettage Specimens

Background:

Digital pathology has been increasingly implemented for primary surgical pathology diagnosis. In our institution, digital pathology was recently deployed in the gynecologic (GYN) pathology practice. A notable challenge encountered in the digital evaluation of GYN specimens was high rates of scanning failure of specimens with fragmented as well as scant tissue. To improve tissue detection failure rates, we implemented a novel use of the collodion bag cell block preparation method.

Materials and Methods:

In this study, we reviewed 108 endocervical curettage (ECC) specimens, representing specimens processed with and without the collodion bag cell block method (n = 56 without collodion bag, n = 52 with collodion bag).

Results:

Tissue detection failure rates were reduced from 77% (43/56) in noncollodion bag cases to 23/52 (44%) of collodion bag cases, representing a 42% reduction. The median total area of tissue detection failure per level was 0.35 mm² (interquartile range [IQR]: 0.14, 0.70 mm²) for noncollodion bag cases and 0.08 mm² (IQR: 0.03, 0.20 mm²) for collodion bag cases. This represents a greater than fourfold reduction in the total area of tissue detection failure per level (P < 0.001). In addition, there were no out-of-focus levels among collodion bag cases, compared to 6/56 (11%) of noncollodion bag cases (median total area = 4.9 mm²).

Conclusions:

The collodion bag method significantly improved the digital image quality of fragmented/scant GYN curettage specimens, increased efficiency and accuracy of diagnostic evaluation, and enhanced identification of tissue contamination during processing. The logistical challenges and labor cost of deploying the collodion bag protocol are important considerations for feasibility assessment at an institutional level.

Guidance for Remote Reporting of Digital Pathology Slides During Periods of Exceptional Service Pressure: An Emergency Response from the UK Royal College of Pathologists

Pathology departments must rise to new staffing challenges caused by the coronavirus disease-19 pandemic and may need to work more flexibly for the foreseeable future. In light of this, many pathologists and departments are considering the merits of remote or home reporting of digital cases. While some individuals have experience of this, little work has been done to determine optimum conditions for home reporting, including technical and training considerations. In this publication produced in response to the pandemic, we provide information regarding risk assessment of home reporting of digital slides, summarize available information on specifications for home reporting computing equipment, and share access to a novel point-of-use quality assurance tool for assessing the suitability of home reporting screens for digital slide diagnosis. We hope this study provides a useful starting point and some practical guidance in a difficult time. This study forms the basis of the guidance issued by the Royal College of Pathologists, available at: https://www.rcpath.org/uploads/assets/626ead77-d7dd-42e1-949988e43dc84c97/RCPath-guidance-for-remote-digital-pathology.pdf.