Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future

TelePi: an affordable telepathology microscope camera system anyone can build and use

Telepathology facilitates histological diagnoses through sharing expertise between pathologists. However, the associated costs are high and frequently prohibitive, especially in low-resource settings, where telepathology would paradoxically be of paramount importance due to a paucity of pathologists.We have constructed a telepathology system (TelePi) with a budget of < €120 using the small, single-board computer Raspberry Pi Zero and its High-Quality Camera Module in conjunction with a standard microscope and open-source software. The system requires no maintenance costs or service contracts, has a small footprint, can be moved and shared across several microscopes, and is independent from other computer operating systems. TelePi uses a responsive and high-resolution web-based live stream which allows remote consultation between two or more locations. TelePi can serve as a telepathology system for remote diagnostics of frozen sections. Additionally, it can be used as a standard microscope camera for teaching of medical students and for basic research. The quality of the TelePi system compared favorable to a commercially available telepathology system that exceed its cost by more than 125-fold. Additionally, still images are of publication quality equal to that of a whole slide scanner that costs 800 times more.In summary, TelePi is an affordable, versatile, and inexpensive camera system that potentially enables telepathology in low-resource settings without sacrificing image quality.

How to Prepare Neuroanatomical Image Data (an Update)

Image data from a single animal in neuroscientific experiments can be comprised of terabytes of information. Full studies can thus be challenging to analyze, store, view, and manage. What follows is an updated guide for preparing and sharing big neuroanatomical image data. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Naming and organizing images and metadata Basic Protocol 2: Preparing and annotating images for presentations and figures Basic Protocol 3: Assessing the internet environment and optimizing images.

Study on an Automatic Classification Method for Determining the Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-Scale Spatial-Spectral Fusion Features

This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial-spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.

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.

Color Standardization and Stain Intensity Calibration for Whole Slide Image-Based Immunohistochemistry Assessment

Automated quantification of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) using whole slide imaging (WSI) is expected to eliminate subjectivity in visual assessment. However, the color intensity in WSI varies depending on the staining process and scanner device. Such variations affect the image analysis results. This paper presents methods to diminish the influence of color variation produced in the staining process using a calibrator slide consisting of peptide-coated microbeads. The calibrator slide is stained along with tissue sample slides, and the 3,3'-diaminobenzidine (DAB) color intensities of the microbeads are used for calibrating the color variation of the sample slides. An off-the-shelf image analysis tool is employed for the automated assessment, in which cells are classified by the thresholds for the membrane staining. We have adopted two methods for calibrating the color variation based on the DAB color intensities obtained from the calibrator slide: (1) thresholds for classifying the DAB membranous intensity are adjusted, and (2) the color intensity of WSI is corrected. In the experiment, the calibrator slides and tissue of breast cancer slides were stained together on different days and used to test our protocol. With the proposed protocol, the discordance in the HER2 evaluation was reduced to one slide out of 120 slides.

Complete digital pathology transition: A large multi-center experience

INTRODUCTION

Transitioning from glass slide pathology to digital pathology for primary diagnostics requires an appropriate laboratory information system, an image management system, and slide scanners; it also reinforces the need for sophisticated pathology informatics including synoptic reporting. Previous reports have discussed the transition itself and relevant considerations for it, but not the selection criteria and considerations for the infrastructure.

OBJECTIVE

To describe the process used to evaluate slide scanners, image management systems, and synoptic reporting systems for a large multisite institution.

METHODS

Six network hospitals evaluated six slide scanners, three image management systems, and three synoptic reporting systems. Scanners were evaluated based on the quality of image, speed, ease of operation, and special capabilities (including z-stacking, fluorescence and others). Image management and synoptic reporting systems were evaluated for their ease of use and capacity.

RESULTS

Among the scanners evaluated, the Leica GT450 produced the highest quality images, while the 3DHistech Pannoramic provided fluorescence and superior z-stacking. The newest generation of scanners, released relatively recently, performed better than slightly older scanners from major manufacturers Although the Olympus VS200 was not fully vetted due to not meeting all inclusion criteria, it is discussed herein due to its exceptional versatility. For Image Management Software, the authors believe that Sectra is, at the time of writing the best developed option, but this could change in the very near future as other systems improve their capabilities. All synoptic reporting systems performed impressively.

CONCLUSIONS

Specifics regarding quality and abilities of different components will change rapidly with time, but large pathology practices considering such a transition should be aware of the issues discussed and evaluate the most current generation to arrive at appropriate conclusions.

Intraoperative assessment of axillary sentinel lymph nodes by telepathology

PURPOSE

Although axillary dissection is no longer indicated for many breast cancer patients with 1-2 positive axillary sentinel lymph nodes (ASLN), intraoperative ASLN assessment is still performed in many institutions for patients undergoing mastectomy or neoadjuvant therapy. With recent advancements in digital pathology, pathologists increasingly evaluate ASLN via remote telepathology. We aimed to compare the performance characteristics of remote telepathology and conventional on-site intraoperative ASLN assessment.

METHODS

Data from ASLN evaluation for breast cancer patients performed at two sites between April 2021 and October 2022 was collated. Remote telepathology consultation was conducted via the Aperio eSlideManager system.

RESULTS

A total of 385 patients were identified during the study period (83 telepathology, 302 on-site evaluations). Although not statistically significant (P = 0.20), the overall discrepancy rate between intraoperative and final diagnoses was slightly higher at 9.6% (8/83) for telepathology compared with 5.3% (16/302) for on-site assessment. Further comparison of performance characteristics of ASLN assessment between telepathology and conventional on-site evaluation revealed no statistically significant differences between deferral rates, discrepancy rates, interpretive or sampling errors, major or minor disagreements, false negative or false positive results as well as clinical impact and turn-around time (P ≥ 0.12).

CONCLUSION

ASLN assessment via telepathology is not significantly different from conventional on-site evaluation, although it shows a slightly higher overall discrepancy rate between intraoperative and final diagnoses (9.6% vs. 5.3%). Further studies are warranted to ensure accuracy of ASLN assessment via telepathology.

Clinical applications of graph neural networks in computational histopathology: A review

Pathological examination is the optimal approach for diagnosing cancer, and with the advancement of digital imaging technologies, it has spurred the emergence of computational histopathology. The objective of computational histopathology is to assist in clinical tasks through image processing and analysis techniques. In the early stages, the technique involved analyzing histopathology images by extracting mathematical features, but the performance of these models was unsatisfactory. With the development of artificial intelligence (AI) technologies, traditional machine learning methods were applied in this field. Although the performance of the models improved, there were issues such as poor model generalization and tedious manual feature extraction. Subsequently, the introduction of deep learning techniques effectively addressed these problems. However, models based on traditional convolutional architectures could not adequately capture the contextual information and deep biological features in histopathology images. Due to the special structure of graphs, they are highly suitable for feature extraction in tissue histopathology images and have achieved promising performance in numerous studies. In this article, we review existing graph-based methods in computational histopathology and propose a novel and more comprehensive graph construction approach. Additionally, we categorize the methods and techniques in computational histopathology according to different learning paradigms. We summarize the common clinical applications of graph-based methods in computational histopathology. Furthermore, we discuss the core concepts in this field and highlight the current challenges and future research directions.

Pathologists' acceptance of telepathology in the Ministry of National Guard Health Affairs Hospitals in Saudi Arabia: A survey study

Introduction

Evaluating users' attitudes and acceptance of telemedicine in the early phases of implementation is critical in predicting a successful adoption and utilization of the service. Telepathology does not require a patient's presence for intercommunication; therefore, it is essential to focus on the acceptance of the main occupational groups that are involved. This study aimed to assess the attitude and acceptance of telepathology in the pathology departments of the Ministry of National Guard Health Affairs (MNGHA) hospitals in Saudi Arabia.

Method

A cross-sectional survey was distributed among pathologists and laboratory technologists in the pathology departments of MNGHA hospitals (N = 78). The data collection instrument was built upon the technology acceptance model's (TAM's) constructs of perceived usefulness (PU), perceived ease of use (PEU), attitude (ATT), and intention to use (ITU).

Results

In total, 64 questionnaires were completed (response rate of 82%). The acceptance levels represented by the median responses to the TAM constructs, varied from 5.5 (slightly agree) to 6 (agree). The availability of digital pathology services in the workplace was significantly correlated with the participants' acceptance of telepathology. There was a strong correlation between ITU and PU and a moderate correlation between PEU and PU.

Conclusion

Results suggest that telepathology is more likely to be adopted if it is considered helpful, therefore, it is recommended to focus on its usefulness and direct benefits during the training period. The participants who were familiar with the concept of digital pathology were more receptive to using telepathology, which might emphasize the importance of introducing and familiarizing the resident with digital health during their training period.

Virtual Microscopy Goes Global: The Images Are Virtual and the Problems Are Real

For the last two centuries, the scholarly education of histology and pathology has been based on technology, initially on the availability of low-cost, high-quality light microscopes, and more recently on the introduction of computers and e-learning approaches to biomedical education. Consequently, virtual microscopy (VM) is replacing glass slides and the traditional light microscope as the main instruments of instruction in histology and pathology laboratories. However, as with most educational changes, there are advantages and disadvantages associated with a new technology. The use of VM for the teaching of histology and pathology requires an extensive infrastructure and the availability of computing devices to all learners, both posing a considerable financial strain on schools and students. Furthermore, there may be valid reasons for practicing healthcare professionals to maintain competency in using light microscopes. In addition, some educators may be reluctant to embrace new technologies. These are some of the reasons why the introduction of VM as an integral part of histology and pathology instruction has been globally uneven. This paper compares the teaching of histology and pathology using traditional or VM in five different countries and their adjacent regions, representing developed, as well as developing areas of the globe. We identify general and local roadblocks to the introduction of this still-emerging didactic technology and outline solutions for overcoming these barriers.

Application of Artificial Intelligence in Pathology: Trends and Challenges

Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence (AI), allowing for image-based diagnosis on the background of digital pathology. There are efforts for developing AI-based tools to save pathologists time and eliminate errors. Here, we describe the elements in the development of computational pathology (CPATH), its applicability to AI development, and the challenges it faces, such as algorithm validation and interpretability, computing systems, reimbursement, ethics, and regulations. Furthermore, we present an overview of novel AI-based approaches that could be integrated into pathology laboratory workflows.

FEEDNet: a feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis

Objective. Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer. However, nuclei segmentation from ‘hematoxylin and eosin’ (HE) stained ‘whole slide images’ (WSIs) remains a challenge due to noise-induced intensity variations and uneven staining. The goal of this paper is to propose a novel deep learning model for accurately segmenting the nuclei in HE-stained WSIs. Approach. We introduce FEEDNet, a novel encoder-decoder network that uses LSTM units and ‘feature enhancement blocks’ (FE-blocks). Our proposed FE-block avoids the loss of location information incurred by pooling layers by concatenating the downsampled version of the original image to preserve pixel intensities. FEEDNet uses an LSTM unit to capture multi-channel representations compactly. Secondly, for datasets that provide class information, we train a multiclass segmentation model, which generates masks corresponding to each class at the output. Using this information, we generate more accurate binary masks than that generated by conventional binary segmentation models. Main results. We have thoroughly evaluated FEEDNet on CoNSeP, Kumar, and CPM-17 datasets. FEEDNet achieves the best value of PQ (panoptic quality) on CoNSeP and CPM-17 datasets and the second best value of PQ on the Kumar dataset. The 32-bit floating-point version of FEEDNet has a model size of 64.90 MB. With INT8 quantization, the model size reduces to only 16.51 MB, with a negligible loss in predictive performance on Kumar and CPM-17 datasets and a minor loss on the CoNSeP dataset. Significance. Our proposed idea of generalized class-aware binary segmentation is shown to be accurate on a variety of datasets. FEEDNet has a smaller model size than the previous nuclei segmentation networks, which makes it suitable for execution on memory-constrained edge devices. The state-of-the-art predictive performance of FEEDNet makes it the most preferred network. The source code can be obtained from https://github.com/CandleLabAI/FEEDNet.

Quantized spiral-phase-modulation based deep learning for real-time defocusing distance prediction

Whole slide imaging (WSI) has become an essential tool in pathological diagnosis, owing to its convenience on remote and collaborative review. However, how to bring the sample at the optimal position in the axial direction and image without defocusing artefacts is still a challenge, as traditional methods are either not universal or time-consuming. Until recently, deep learning has been shown to be effective in the autofocusing task in predicting defocusing distance. Here, we apply quantized spiral phase modulation on the Fourier domain of the captured images before feeding them into a light-weight neural network. It can significantly reduce the average predicting error to be lower than any previous work on an open dataset. Also, the high predicting speed strongly supports it can be applied on an edge device for real-time tasks with limited computational source and memory footprint.

Cancer in sub-Saharan Africa: a Lancet Oncology Commission

In sub-Saharan Africa (SSA), urgent action is needed to curb a growing crisis in cancer incidence and mortality. Without rapid interventions, data estimates show a major increase in cancer mortality from 520 348 in 2020 to about 1 million deaths per year by 2030. Here, we detail the state of cancer in SSA, recommend key actions on the basis of analysis, and highlight case studies and successful models that can be emulated, adapted, or improved across the region to reduce the growing cancer crises. Recommended actions begin with the need to develop or update national cancer control plans in each country. Plans must include childhood cancer plans, managing comorbidities such as HIV and malnutrition, a reliable and predictable supply of medication, and the provision of psychosocial, supportive, and palliative care. Plans should also engage traditional, complementary, and alternative medical practices employed by more than 80% of SSA populations and pathways to reduce missed diagnoses and late referrals. More substantial investment is needed in developing cancer registries and cancer diagnostics for core cancer tests. We show that investments in, and increased adoption of, some approaches used during the COVID-19 pandemic, such as hypofractionated radiotherapy and telehealth, can substantially increase access to cancer care in Africa, accelerate cancer prevention and control efforts, increase survival, and save billions of US dollars over the next decade. The involvement of African First Ladies in cancer prevention efforts represents one practical approach that should be amplified across SSA. Moreover, investments in workforce training are crucial to prevent millions of avoidable deaths by 2030. We present a framework that can be used to strategically plan cancer research enhancement in SSA, with investments in research that can produce a return on investment and help drive policy and effective collaborations. Expansion of universal health coverage to incorporate cancer into essential benefits packages is also vital. Implementation of the recommended actions in this Commission will be crucial for reducing the growing cancer crises in SSA and achieving political commitments to the UN Sustainable Development Goals to reduce premature mortality from non-communicable diseases by a third by 2030.

Deep Learning Analysis of the Adipose Tissue and the Prediction of Prognosis in Colorectal Cancer

Research has shown that the lipid microenvironment surrounding colorectal cancer (CRC) is closely associated with the occurrence, development, and metastasis of CRC. According to pathological images from the National Center for Tumor diseases (NCT), the University Medical Center Mannheim (UMM) database and the ImageNet data set, a model called VGG19 was pre-trained. A deep convolutional neural network (CNN), VGG19CRC, was trained by the migration learning method. According to the VGG19CRC model, adipose tissue scores were calculated for TCGA-CRC hematoxylin and eosin (H&E) images and images from patients at Zhujiang Hospital of Southern Medical University and First People's Hospital of Chenzhou. Kaplan-Meier (KM) analysis was used to compare the overall survival (OS) of patients. The XCell and MCP-Counter algorithms were used to evaluate the immune cell scores of the patients. Gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) were used to analyze upregulated and downregulated pathways. In TCGA-CRC, patients with high-adipocytes (high-ADI) CRC had significantly shorter OS times than those with low-ADI CRC. In a validation queue from Zhujiang Hospital of Southern Medical University (Local-CRC1), patients with high-ADI had worse OS than CRC patients with low-ADI. In another validation queue from First People's Hospital of Chenzhou (Local-CRC2), patients with low-ADI CRC had significantly longer OS than patients with high-ADI CRC. We developed a deep convolution network to segment various tissues from pathological H&E images of CRC and automatically quantify ADI. This allowed us to further analyze and predict the survival of CRC patients according to information from their segmented pathological tissue images, such as tissue components and the tumor microenvironment.

Reconstructing virtual large slides can improve the accuracy and consistency of tumor bed evaluation for breast cancer after neoadjuvant therapy

BACKGROUND

To explore whether the "WSI Stitcher", a program we developed for reconstructing virtual large slide through whole slide imaging fragments stitching, can improve the efficiency and consistency of pathologists in evaluating the tumor bed after neoadjuvant treatment of breast cancer compared with the conventional methods through stack splicing of physical slides.

METHODS

This study analyzed the advantages of using software-assisted methods to evaluate the tumor bed after neoadjuvant treatment of breast cancer. This new method is to use "WSI Stitcher" to stitch all the WSI fragments together to reconstruct a virtual large slide and evaluate the tumor bed with the help of the built-in ruler and tumor proportion calculation functions.

RESULTS

Compared with the conventional method, the evaluation time of the software-assisted method was shortened by 35%(P < 0.001). In the process of tumor bed assessment after neoadjuvant treatment of breast cancer, the software-assisted method has higher intraclass correlation coefficient when measuring the length (0.994 versus 0.934), width (0.992 versus 0.927), percentage of residual tumor cells (0.947 versus 0.878), percentage of carcinoma in situ (0.983 versus 0.881) and RCB index(0.997 versus 0.772). The software-assisted method has higher kappa values when evaluating tumor staging(0.901 versus 0.687) and RCB grading (0.963 versus 0.857).

CONCLUSION

The "WSI Stitcher" is an effective tool to help pathologists with the assessment of breast cancer after neoadjuvant treatment.

Learning to autofocus in whole slide imaging via physics-guided deep cascade networks

Whole slide imaging (WSI), is an essential technology for digital pathology, the performance of which is primarily affected by the autofocusing process. Conventional autofocusing methods either are time-consuming or require additional hardware and thus are not compatible with the current WSI systems. In this paper, we propose an effective learning-based method for autofocusing in WSI, which can realize accurate autofocusing at high speed as well as without any optical hardware modifications. Our method is inspired by an observation that sample images captured by WSI have distinctive characteristics with respect to positive / negative defocus offsets, due to the asymmetry effect of optical aberrations. Based on this physical knowledge, we develop novel deep cascade networks to enhance autofocusing quality. Specifically, to handle the effect of optical aberrations, a binary classification network is tailored to distinguish sample images with positive / negative defocus. As such, samples within the same category share similar characteristics. It facilitates the followed refocusing network, which is designed to learn the mapping between the defocus image and defocus distance. Experimental results demonstrate that our method achieves superior autofocusing performance to other related methods.

Videoconference microscopy is a reliable alternative to conventional microscopy in the evaluation of Barrett's esophagus: Zooming into a new era

Telepathology, practicing pathology from a distance, allows experts to review cases without the need to transfer glass slides. Due to significant intra- and inter-observer variabilities in the histological evaluation of Barrett's esophagus (BE), current guidelines recommend expert consultation in cases of dysplasia. We aimed to determine whether telepathology using microscope videoconferencing can be reliably used for evaluation of BE. Biopsies from 62 patients with endoscopic findings of salmon colored mucosa extending ≥1 cm proximal to the gastroesophageal junction were randomly selected to represent benign esophagus, non-dysplastic BE, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma. Three gastrointestinal-trained pathologists reviewed the cases via videoconference microscopy followed by conventional microscopy. Intra-observer and pairwise inter-observer agreements between the conventional microscopy and videoconference methodologies were calculated for each of the three pathologists using Fleiss-Cohen weighted kappa (K) analysis. The intra-observer agreement for each pathologist's assessment of videoconference microscopy and glass slide readings showed very good reliability (K = 0.94, 95% confidence interval = 0.89-0.99; 0.88, 95% confidence interval = 0.79-0.98; 0.93, 95% confidence interval = 0.90-0.97). Mean pairwise inter-observer agreement was 0.90 for videoconference and 0.91 for conventional microscopy. Diagnosis and grading of BE using videoconference microscopy show similar reliability as conventional microscopy. Based on our findings, we propose that videoconferencing pathology is a valid instrument for evaluating BE.

FE-learning and the virtual transformation of histopathology teaching during COVID-19: its impact on student learning experience and outcome

BACKGROUND

Medical and pathology education has gone through an immense transformation from traditional face-to-face teaching mode to virtual mode during the COVID-19 pandemic. This study evaluated the effectiveness of online histopathology teaching in medical education during the 2020 COVID-19 pandemic in Griffith University, Australia.

METHODS

Second-year medical students (n = 150) who had previously completed one year of face-to-face histopathology teaching, completed an online questionnaire rating their learning experiences before and during the COVID-19 pandemic after the completion of their histology and pathology practical sessions. The students' histopathology assessment results were then compared to the histopathology results of a prior second-year cohort to determine if the switch to online histopathology teaching had an impact on students' learning outcome.

RESULTS

A thematic analysis of the qualitative comments strongly indicated that online histopathology teaching was instrumental, more comfortable to engage in and better structured compared to face-to-face teaching. Compared to the previous year's practical assessment, individual performance was not significantly different (p = 0.30) and compared to the prior cohort completing the same curriculum the mean overall mark was significantly improved from 65.36% ± 13.12% to 75.83% ± 14.84% (p < 0.05) during the COVID-19 impacted online teaching period.

CONCLUSIONS

The transformation of teaching methods during the 2020 COVID-19 pandemic improved student engagement without any adverse effects on student learning outcomes in histology and pathology education.

Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future

Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) plaques in the brain. The disease was first described in 1906 by Alois Alzheimer, and since then, there have been many advancements in technologies that have aided in unlocking the secrets of this devastating disease. Such advancements include improving microscopy and staining techniques, refining diagnostic criteria for the disease, and increased appreciation for disease heterogeneity both in neuroanatomic location of abnormalities as well as overlap with other brain diseases; for example, Lewy body disease and vascular dementia. Despite numerous advancements, there is still much to achieve as there is not a cure for AD and postmortem histological analyses is still the gold standard for appreciating AD neuropathologic changes. Recent technological advances such as in-vivo biomarkers and machine learning algorithms permit great strides in disease understanding, and pave the way for potential new therapies and precision medicine approaches. Here, we review the history of human AD neuropathology research to include the notable advancements in understanding common co-pathologies in the setting of AD, and microscopy and staining methods. We also discuss future approaches with a specific focus on deep phenotyping using machine learning.

The potential role of telemedicine in early detection of oral cancer: A literature review

The 5-year survival rates of oral cancer have not improved significantly since many decades. It is believed that “diagnostic delay” plays a critical role in determining the prognostic outcomes. At present, the coronavirus disease (COVID-19) pandemic has led to drastic changes within a short period of time and has resulted in many serious consequences at different levels worldwide. Although it is evident that COVID-19 is a major concern when it comes to people's health, it carries with it a message of hope, a desire to survive, and an opportunity to address many unprecedented challenges. This has left the doors wide open to use “telemedicine” as an essential tool to counter the rapid shift in health-care services and to meet the high demands in different health specialties including oral medicine. The aim of this review is to explore the potential roles of telemedicine in early detection of oral cancer and to highlight both the benefits and the limitations of the available applications and technologies. The clinical applications of telemedicine show a great potential in early detection of oral cancer, but the evidence of their effectiveness is still not conclusive. This needs to be investigated, especially in the developing countries where “telemedicine” may prove to be highly valuable in the future.

Types and frequency of whole slide imaging scan failures in a clinical high throughput digital pathology scanning laboratory

Digital workflow transformation continues to sweep throughout a diversity of pathology departments spanning the globe following catalyzation of whole slide imaging (WSI) adoption by the SARS-CoV-2 (COVID-19) pandemic. The utility of WSI for a litany of use cases including primary diagnosis has been emphasized during this period, with WSI scanning devices gaining the approval of healthcare regulatory bodies and practitioners alike for clinical applications following extensive validatory efforts. As successful validation for WSI is predicated upon pathologist diagnostic interpretability of digital images with high glass slide concordance, departmental adoption of WSI is tantamount to the reliability of such images often predicated upon quality assessment notwithstanding image interpretability but extending to quality of practice following WSI adoption. Metrics of importance within this context include failure rates inclusive of different scanning errors that result in poor image quality and the potential such errors may incur upon departmental turnaround time (TAT). We sought to evaluate the impact of WSI implementation through retrospective evaluation of scan failure frequency in archival versus newly prepared slides, types of scanning error, and impact upon TAT following commencement of live WSI operation in May 2017 until the present period within a fully digitized high-volume academic institution. A 1.19% scan failure incidence rate was recorded during this period, with re-scanning requested and successfully executed for 1.19% of cases during the reported period of January 2019 until present. No significant impact upon TAT was deduced, suggesting an outcome which may be encouraging for departments considering digital workflow adoption.

Frozen section telepathology service: Efficiency and benefits of an e-health policy in South Tyrol

Objective/Background

Telepathology has been widely adopted to allow intraoperative pathology examinations to be performed remotely and for obtaining second opinion teleconsultation. In the Italian northern region of South Tyrol, the widespread geographical distances and consequent cost for the health system of having a travelling pathologist cover intraoperative consultations in peripheral hospitals was a key driver for the implementation of a telepathology system.

Methods

In 2010, four Menarini D-Sight whole slide scanners to digitize entire pathology slides were placed in the peripheral hospitals of Merano, Bressanone, Brunico, and in the hub hospital of Bolzano. Digital workstations were also installed to allow pathologists to remotely perform intraoperative consultations with digital slides. This study reviews the outcome after 12 years of telepathology for this intended clinical use.

Results

After an initial validation phase with 100 cases which yielded a sensitivity of 65% (CI 43–84%) and specificity of 100% (CI 95–100%), there were 2058 intraoperative consultations handled by telepathology. The cases evaluated were mainly breast sentinel lymph nodes, followed by urological, gynecological and general surgical pathology frozen section specimens. There were no false-positive cases and 165 (8%) false-negative cases, yielding an overall sensitivity and specificity of 65% (CI 61–69%) and 100% (CI 99–100%), respectively.

Conclusion

Telepathology is reliable for remote intraoperative diagnosis and, despite technical issues and initial acquaintance issues, proved beneficial for patient care in satellite hospitals, improved standardization, promoted innovation, and resulted in cost savings for the health system.

Detection of malignant melanoma in H&E-stained images using deep learning techniques

Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin (H&E) stained histopathological images. In this technique, the nuclei in the image are first segmented using a Convolutional Neural Network (CNN). The segmented nuclei are then used to generate melanoma region masks. Experimental results with a small melanoma dataset show that the proposed method can potentially segment the nuclei with more than 94 % accuracy and segment the melanoma regions with a Dice coefficient of around 85 %. The proposed technique also has a small execution time making it suitable for clinical diagnosis with a fast turnaround time.

An efficient CNN based algorithm for detecting melanoma cancer regions in H&E-stained images

Histopathological images are widely used to diagnose diseases such as skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of abnormal cell nuclei and their distribution within multiple tissue sections would enable rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin-stained histopathological images. In this technique, the nuclei in an image are first segmented using a deep learning neural network. The segmented nuclei are then used to generate the melanoma region masks. Experimental results show that the proposed method can provide nuclei segmentation accuracy of around 90% and the melanoma region segmentation accuracy of around 98%. The proposed technique also has a low computational complexity.

SOFTWARE-ASSISTED IMAGE ANALYSIS FOR IDENTIFICATION AND QUANTIFICATION OF HEPATIC SINUSOIDAL DILATATION AND CENTRILOBULAR FIBROSIS

Background:

Heart dysfunction and liver disease often coexist because of systemic disorders. Any cause of right ventricular failure may precipitate hepatic congestion and fibrosis. Digital image technologies have been introduced to pathology diagnosis, allowing an objective quantitative assessment. The quantification of fibrous tissue in liver biopsy sections is extremely important in the classification, diagnosis and grading of chronic liver disease.

Aim:

To create a semi-automatic computerized protocol to quantify any amount of centrilobular fibrosis and sinusoidal dilatation in liver Masson’s Trichrome-stained specimen.

Method:

Once fibrosis had been established, liver samples were collected, histologically processed, stained with Masson’s trichrome, and whole-slide images were captured with an appropriated digital pathology slide scanner. After, a random selection of the regions of interest (ROI’s) was conducted. The data were subjected to software-assisted image analysis (ImageJ^®).

Results:

The analysis of 250 ROI’s allowed to empirically obtain the best application settings to identify the centrilobular fibrosis (CF) and sinusoidal lumen (SL). After the establishment of the colour threshold application settings, an in-house Macro was recorded to set the measurements (fraction area and total area) and calculate the CF and SL ratios by an automatic batch processing.

Conclusion:

Was possible to create a more detailed method that identifies and quantifies the area occupied by fibrous tissue and sinusoidal lumen in Masson’s trichrome-stained livers specimens.

Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images

With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating high-throughput analysis of image data in a variety of biomedical imaging disciplines, ranging from radiology and pathology to cancer biology and immunology. Specifically, a growth in research on deep learning has led to the widespread application of computer-visualization techniques for analyzing and mining data from biomedical images. The availability of open-source software packages and the development of novel, trainable deep neural network architectures has led to increased accuracy in cell detection and segmentation algorithms. By automating cell segmentation, it is now possible to mine quantifiable cellular and spatio-cellular features from microscopy images, providing insight into the organization of cells in various pathologies. This mini-review provides an overview of the current state of the art in deep learning- and artificial intelligence-based methods of segmentation and data mining of cells in microscopy images of tissue.

Virtual Microscopy in Undergraduate Pathology Education: An early transformative experience in clinical reasoning

Objectives

Whole-slide imaging and virtual microscopy (VM) have revolutionised teaching, diagnosis and research in histopathology. This study aimed to establish the feasibility of achieving early integration of clinical reasoning with undergraduate pathology teaching on a VM platform and to determine its student-centricity through student feedback.

Methods

This study was conducted at the Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates, between August and December 2017. A total of 38 VM-centred clinical cases were introduced to 49 students in an integrated undergraduate medical curriculum. The cases were aligned to curricular objectives, reinforced the pathologic basis of disease with critical thinking and were delivered across 15 interactive small-group sessions. A simulated cross-disciplinary integration and judicious choice of pertinent diagnostic investigations were linked to principles of management. Feedback was obtained through a mixed-methods approach.

Results

User-friendliness, gradual learning curve of VM and annotation-capacity were scored as 4–5 (on a Likert scale of 1–5) by 91.84%, 87.76% and 83.67% of the participants, respectively. Most students agreed that the content matched the stage of learning (81.63%), theme of the week (91.84%) and development of a strong clinical foundation (77.55%). Integration (85.71%) and clinico-pathological correlation (83.67%) were the strengths of this educational effort. High student attendance (~100%) and improved assessment scores on critical thinking (80%) were observed. Software lacunae included frequent logouts and lack of note-taking tools. Easy access was a significant student-centric advantage.

Conclusion

A VM-centred approach with a clinico-pathological correlation has been successfully introduced to inculcate integrated learning. Using the pathologic basis of disease as a fulcrum and critical reasoning as an anchor, a digitally-enabled generation of medical students have embraced this educational tool for tutor-guided, student-centred learning.

Flattening the World of Pathology Education and Training and Shortening the Curve of Pathology Learning

OBJECTIVES

We review how the pandemic-related education disruption may interplay with pathology manpower worldwide and shifts in disease burden to identify workable solutions.

METHODS

Literature related to pathology education, pathology services in low-resource settings, and application of digital tools to pathology education was reviewed for trends and training gaps. Publications covering pathology manpower and cancer incidence worldwide were also included to assess needs.

RESULTS

Pandemic-related virtual teaching has produced abundant online training materials. Pathology learning resources in low- to middle-income countries remain considerably constrained and dampen pathology manpower growth to meet current needs. Projected increases in disease burden toward the developing world thus pose a major challenge. Digital pathology resources have expanded and are beginning to appear beyond the developed countries.

CONCLUSIONS

This circumstance offers a unique opportunity to leverage digital teaching resources to enhance and equitize training internationally, potentially sufficient to meet the rising wave of noncommunicable diseases. We propose four next steps to take advantage of the current opportunity: curate and organize digital training materials, invest in the digital pathology infrastructure for education and clinical care, expand student exposure to pathology through virtual electives, and develop further competency-based certification pathways.

Continuing Undergraduate Pathology Medical Education in the Coronavirus Disease 2019 (COVID-19) Global Pandemic: The Johns Hopkins Virtual Surgical Pathology Clinical Elective

CONTEXT.—

In the early months of the response to the coronavirus disease 2019 (COVID-19) pandemic, the Johns Hopkins University School of Medicine (JHUSOM) (Baltimore, Maryland) leadership reached out to faculty to develop and implement virtual clinical clerkships after all in-person medical student clinical experiences were suspended.

OBJECTIVE.—

To develop and implement a digital slide-based virtual surgical pathology (VSP) clinical elective to meet the demand for meaningful and robust virtual clinical electives in response to the temporary suspension of in-person clinical rotations at JHUSOM.

DESIGN.—

The VSP elective was modeled after the in-person surgical pathology elective to include virtual previewing and sign-out with standardized cases supplemented by synchronous and asynchronous pathology educational content.

RESULTS.—

Validation of existing Web communications technology and slide-scanning systems was performed by feasibility testing. Curriculum development included drafting of course objectives and syllabus, Blackboard course site design, electronic-lecture creation, communications with JHUSOM leadership, scheduling, and slide curation. Subjectively, the weekly schedule averaged 35 to 40 hours of asynchronous, synchronous, and independent content, approximately 10 to 11 hours of which were synchronous. As of February 2021, VSP has hosted 35 JHUSOM and 8 non-JHUSOM students, who have provided positive subjective and objective course feedback.

CONCLUSIONS.—

The Johns Hopkins VSP elective provided meaningful clinical experience to 43 students in a time of immense online education need. Added benefits of implementing VSP included increased medical student exposure to pathology as a medical specialty and demonstration of how digital slides have the potential to improve standardization of the pathology clerkship curriculum.

Smartpathk: a platform for teaching glomerulopathies using machine learning

BACKGROUND

With the emergence of the new coronavirus pandemic (COVID-19), distance learning, especially that mediated by information and digital communication technologies, has been adopted in all areas of knowledge and at all levels, including medical education. Imminently practical areas, such as pathology, have made traditional teaching based on conventional microscopy more flexible through the synergies of computational tools and image digitization, not only to improve teaching-learning but also to offer alternatives to repetitive and exhaustive histopathological analyzes. In this context, machine learning algorithms capable of recognizing histological patterns in kidney biopsy slides have been developed and validated with a view to building computational models capable of accurately identifying renal pathologies. In practice, the use of such algorithms can contribute to the universalization of teaching, allowing quality training even in regions where there is a lack of good nephropathologists. The purpose of this work is to describe and test the functionality of SmartPath^k, a tool to support teaching of glomerulopathies using machine learning. The training for knowledge acquisition was performed automatically by machine learning methods using the J48 algorithm to create a computational model of an appropriate decision tree.

RESULTS

An intelligent system, SmartPath^k, was developed as a complementary remote tool in the teaching-learning process for pathology teachers and their students (undergraduate and graduate students), showing 89,47% accuracy using machine learning algorithms based on decision trees.

CONCLUSION

This artificial intelligence system can assist in teaching renal pathology to increase the training capacity of new medical professionals in this area.

Residual cyclegan for robust domain transformation of histopathological tissue slides

Variation between stains in histopathology is commonplace across different medical centers. This can have a significant effect on the reliability of machine learning algorithms. In this paper, we propose to reduce performance variability by using -consistent generative adversarial (CycleGAN) networks to remove staining variation. We improve upon the regular CycleGAN by incorporating residual learning. We comprehensively evaluate the performance of our stain transformation method and compare its usefulness in addition to extensive data augmentation to enhance the robustness of tissue segmentation algorithms. Our steps are as follows: first, we train a model to perform segmentation on tissue slides from a single source center, while heavily applying augmentations to increase robustness to unseen data. Second, we evaluate and compare the segmentation performance on data from other centers, both with and without applying our CycleGAN stain transformation. We compare segmentation performances in a colon tissue segmentation and kidney tissue segmentation task, covering data from 6 different centers. We show that our transformation method improves the overall Dice coefficient by 9% over the non-normalized target data and by 4% over traditional stain transformation in our colon tissue segmentation task. For kidney segmentation, our residual CycleGAN increases performance by 10% over no transformation and around 2% compared to the non-residual CycleGAN.

Identification and Validation of Efficacy of Immunological Therapy for Lung Cancer From Histopathological Images Based on Deep Learning

Cancer immunotherapy, as a novel treatment against cancer metastasis and recurrence, has brought a significantly promising and effective therapy for cancer treatments. At present, programmed death 1 (PD-1) and programmed cell death-Ligand 1 (PD-L1) treatment for lung cancer is primarily recognized as an immune checkpoint inhibitor (ICI) to play an anti-tumor effect; however, it remains uncertain regarding of its efficacy though. Thereafter, tumor mutation burden (TMB) was recognized as a high-potential to be a predictive marker for the immune therapy, but it is invasive and costly. Therefore, discovering more immune-related biomarkers that have a guiding role in immunotherapy is a crucial step in the development of immunotherapy. In our study, we proposed a deep convolutional neural network (CNN)-based framework, DeepLRHE, which can efficiently analyze immunological stained pathological images of lung cancer tissues, as well as to identify and explore pathogenesis which can be used for immunological treatment in clinical field. In this study, we used 180 whole slice images (WSIs) of lung cancer downloaded from TCGA which was model training and validation. After two cross-validation used for this model, we compared with the area under the curve (AUC) of multiple mutant genes, TP53 had highest AUC, which reached 0.87, and EGFR, DNMT3A, PBRM1, STK11 also reached ranged from 0.71 to 0.84. The study results showed that the deep learning can used to assist health professionals for target-therapy as well as immunotherapies, therefore to improve the disease prognosis.

Interchangeability of light and virtual microscopy for histopathological evaluation of prostate cancer

Virtual microscopy (VM) holds promise to reduce subjectivity as well as intra- and inter-observer variability for the histopathological evaluation of prostate cancer. We evaluated (i) the repeatability (intra-observer agreement) and reproducibility (inter-observer agreement) of the 2014 Gleason grading system and other selected features using standard light microscopy (LM) and an internally developed VM system, and (ii) the interchangeability of LM and VM. Two uro-pathologists reviewed 413 cores from 60 Swedish men diagnosed with non-metastatic prostate cancer 1998–2014. Reviewer 1 performed two reviews using both LM and VM. Reviewer 2 performed one review using both methods. The intra- and inter-observer agreement within and between LM and VM were assessed using Cohen’s kappa and Bland and Altman’s limits of agreement. We found good repeatability and reproducibility for both LM and VM, as well as interchangeability between LM and VM, for primary and secondary Gleason pattern, Gleason Grade Groups, poorly formed glands, cribriform pattern and comedonecrosis but not for the percentage of Gleason pattern 4. Our findings confirm the non-inferiority of VM compared to LM. The repeatability and reproducibility of percentage of Gleason pattern 4 was poor regardless of method used warranting further investigation and improvement before it is used in clinical practice.

The Use of Health-Related Technology to Reduce the Gap Between Developed and Undeveloped Regions Around the Globe

Cancer is the second leading cause of death worldwide, with approximately 70% of the 9.6 million deaths per year occurring in low- and middle-income countries (LMICs), where there is critical shortage of human and material resources or infrastructure to deal with cancer. If the current trend continues, the burden of cancer is expected to increase to 22 million new cases annually by 2030, with 81% of new cases and almost 88% of mortality occurring in LMICs. Global health places a priority on improving health and reducing these disparities to achieve equity in health for all people worldwide. In today's hyper-connected world, information and communication technologies (ICTs) will increasingly play an integral role in global health. Here, we focus on how the use of health-related technology, specifically ICTs and artificial intelligence (AI), can help in closing the gap between high-income countries (HICs) and LMICs in cancer care, research, and education. Key examples are highlighted on the use of telemedicine and tumor boards, as well as other online resources that can be leveraged to advance global health.

Autofocus of whole slide imaging based on convolution and recurrent neural networks

During the process of whole slide imaging, it is necessary to focus thousands of fields of view to obtain a high-quality image. To make the focusing procedure efficient and effective, we propose a novel autofocus algorithm for whole slide imaging. It is based on convolution and recurrent neural networks to predict the out-of-focus distance and subsequently update the focus location of the camera lens in an iterative manner. More specifically, we train a convolution neural network to extract focus information in the form of a focus feature vector. In order to make the prediction more accurate, we apply a recurrent neural network to combine focus information from previous search iteration and current search iteration to form a feature aggregation vector. This vector contains more focus information than the previous one and is subsequently used to predict the out-of-focus distance. Our experiments indicate that our proposed autofocus algorithm is able to rapidly determine the optimal in-focus image. The code is available at https://github.com/hezhujun/autofocus-rnn.

Telepathology Support System with Gross Specimen Image Using High Resolution 4K Multispectral Camera

This study reports on the development of a high-resolution 4K multispectral camera designed to enhance telepathology support systems for remote gross-pathological diagnosis. We experimentally examine and evaluate the camera's effectiveness in three subjects: the reconstruction of precise color images, the emphasis of cancerous tissue areas, and pre-fixed image reproduction from fixed images. The evaluation results of the first and second subjects showed that the camera and supporting methods could be effectively used in gross pathology diagnosis. The images obtained in the third subject received positive evaluations from some pathologists, but others expressed reservations as to its utility.

Deep learning in digital pathology image analysis: a survey

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.

On Edge Computing for Remote Pathology Consultations and Computations

Telepathology aims to replace the pathology operations performed on-site, but current systems are limited by their prohibitive cost, or by the adopted underlying technologies. In this work, we contribute to overcoming these limitations by bringing the recent advances of edge computing to reduce latency and increase local computation abilities to the pathology ecosystem. In particular, this paper presents LiveMicro, a system whose benefit is twofold: on one hand, it enables edge computing driven digital pathology computations, such as data-driven image processing on a live capture of the microscope. On the other hand, our system allows remote pathologists to diagnosis in collaboration in a single virtual microscope session, facilitating continuous medical education and remote consultation, crucial for under-served and remote hospital or private practice. Our results show the benefits and the principles underpinning our solution, with particular emphasis on how the pathologists interact with our application. Additionally, we developed simple yet effective diagnosis-aided algorithms to demonstrate the practicality of our approach.

The future of pathology is digital

Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of cases' referral information, as needed for example, by the pathologist, and this support will be increased in the future even more. In radiology, established standards define information models, data transmission mechanisms, and workflows. Other disciplines, such as pathology, cardiology, and radiation therapy, now define further demands in addition to these established standards. Pathology may have the highest technical demands on the systems, with very complex workflows, and the digitization of slides generating enormous amounts of data up to Gigabytes per biopsy. This requires enormous amounts of data to be generated per biopsy, up to the gigabyte range. Digital pathology allows a change from classical histopathological diagnosis with microscopes and glass slides to virtual microscopy on the computer, with multiple tools using artificial intelligence and machine learning to support pathologists in their future work.

Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis

PURPOSE

Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation of tissue primitives (eg, nuclei, glands, lymphocytes). One application of DP is in telepathology, which involves digitally transmitting DP slides over the Internet for secondary diagnosis by an expert at a remote location. Unfortunately, the places benefiting most from telepathology often have poor Internet quality, resulting in prohibitive transmission times of DP images. Image compression may help, but the degree to which image compression affects performance of DL algorithms has been largely unexplored.

METHODS

We investigated the effects of image compression on the performance of DL strategies in the context of 3 representative use cases involving segmentation of nuclei (n = 137), segmentation of lymph node metastasis (n = 380), and lymphocyte detection (n = 100). For each use case, test images at various levels of compression (JPEG compression quality score ranging from 1-100 and JPEG2000 compression peak signal-to-noise ratio ranging from 18-100 dB) were evaluated by a DL classifier. Performance metrics including F1 score and area under the receiver operating characteristic curve were computed at the various compression levels.

RESULTS

Our results suggest that DP images can be compressed by 85% while still maintaining the performance of the DL algorithms at 95% of what is achievable without any compression. Interestingly, the maximum compression level sustainable by DL algorithms is similar to where pathologists also reported difficulties in providing accurate interpretations.

CONCLUSION

Our findings seem to suggest that in low-resource settings, DP images can be significantly compressed before transmission for DL-based telepathology applications.

The California Telepathology Service: UCLA's Experience in Deploying a Regional Digital Pathology Subspecialty Consultation Network

Background:

The need for extending pathology diagnostic expertise to more areas is now being met by the maturation of technology that can effectively deliver this level of care. The experience and lessons learned from our successfully deployed International Telepathology Service (ITS) to a hospital system in China guided us in starting a domestic telepathology network, the California Telepathology Service (CTS). Many of the lessons learned from the ITS project informed our decision-making for the CTS. New challenges were recognized and overcome, such as addressing the complexity and cost–benefit tradeoffs involved in setting up a digital consultation system that competes with an established conventional glass slide delivery system.

Methods:

The CTS is based on a hub-and-spoke telepathology network using Leica Biosystems whole-slide image scanners and the eSlide Manager (eSM Version 12.3.3.7055, Leica Biosystems) digital image management software solution. The service currently comprises six spoke sites (UC San Diego [UCSD], UC Irvine [UCI], UC Davis, Northridge Hospital Medical Center [NHMC], Olive View Medical Center [OVMC], and Children's Hospital Los Angeles) and one central hub site (UCLA Medical Center). So far, five sites have been validated for telepathology case consultations following established practice guidelines, and four sites (UCI, UCSD, NHMC, and OVMC) have activated the service.

Results:

For the active spoke sites, we reviewed the volume, turnaround time (TAT), and case types and evaluated for utility and value. From May 2017 to July 2018, a total of 165 cases were submitted. Of note, digital consultations were particularly advantageous for preliminary kidney biopsy diagnoses (avg TAT 0.7 day).

Conclusion:

For spoke sites, telepathology provided shortened TAT and significant financial savings over hiring faculty with expertise to support a potentially low-volume service. For the hub site, the value includes exposure to educationally valuable cases, additional caseload volume to support specialized services, and improved communication with referring facilities over traditional carrier mail. The creation of a hub-and-spoke telepathology network is an expensive undertaking, and careful consideration needs to be given to support the needs of the clinical services, acquisition and effective deployment of the appropriate equipment, network requirements, and laboratory workflows to ensure a successful and cost-effective system.

Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra

Hyperspectral imaging (HSI) provides more detailed information than red-green-blue (RGB) imaging, and therefore has potential applications in computer-aided pathological diagnosis. This study aimed to develop a pattern recognition method based on HSI, called hyperspectral analysis of pathological slides based on stain spectrum (HAPSS), to detect cancers in hematoxylin and eosin-stained pathological slides of pancreatic tumors. The samples, comprising hyperspectral cubes of 420-750 nm, were harvested for HSI and tissue microarray (TMA) analysis. As a result of conducting HAPSS experiments with a support vector machine (SVM) classifier, we obtained maximal accuracy of 94%, a 14% improvement over the widely used RGB images. Thus, HAPSS is a suitable method to automatically detect tumors in pathological slides of the pancreas.

A validation study of whole slide imaging for primary diagnosis of lymphoma

Whole slide imaging (WSI) is being increasingly used worldwide. Although previous studies have asserted the validity of WSI diagnosis, they have primarily targeted only small specimens and excluded cases requiring immunohistochemistry or special staining, such as lymphoma. The purpose of this study was to evaluate the accuracy of WSI diagnosis of lymphoma, for which 240 biopsies and resections of lymphoma cases were selected from the study set of lymphomas. All slides including H&E, immunohistochemical and special staining were digitized using a WSI image scanner. An experienced pathologist performed the WSI diagnoses, which were compared with original diagnoses based on light microscopic examinations. Discrepancy between the two interpretations were classified into three categories: concordance, minor discrepancy (no clinical significance), and major discrepancy (with clinical significance). Overall concordance between the light microscopic and WSI diagnosis was found in 223 cases (92.92%; 95%CI = 88.90-95.82), minor discrepancy in fifteen (6.25%; 95%CI = 3.54-10.10), and major discrepancy in two (0.83%; 95%CI = 0.10-2.98). Diagnosis of lymphoma using WSI appeared to be mostly accurate, suggesting that WSI may be a reliable technology for the diagnosis of lymphoma.