Digital reporting of whole-slide images is safe and suitable for assessing organ quality in preimplantation renal biopsies

The Puzzle of Preimplantation Kidney Biopsy Decision-Making Process: The Pathologist Perspective

Kidney transplantation is the best treatment for end-stage renal disease since it offers the greatest survival benefit compared to dialysis. The gap between the number of renal transplants performed and the number of patients awaiting renal transplants leads to a steadily increasing pressure on the scientific community. Kidney preimplantation biopsy is used as a component of the evaluation of organ quality before acceptance for transplantation. However, the reliability and predictive value of biopsy data are controversial. Most of the previously proposed predictive models were not associated with graft survival, but what has to be reaffirmed is that histologic examination of kidney tissue can provide an objective window on the state of the organ that cannot be deduced from clinical records and renal functional studies. The balance of evidence indicates that reliable decisions about donor suitability must be made based on the overall picture. This work discusses recent trends that can reduce diagnostic timing and variability among players in the decision-making process that lead to kidney transplants, from the pathologist's perspective.

Artificial Intelligence and liver: Opportunities and barriers

Artificial Intelligence (AI) has recently been shown as an excellent tool for the study of the liver; however, many obstacles still have to be overcome for the digitalization of real-world hepatology. The authors present an overview of the current state of the art on the use of innovative technologies in different areas (big data, translational hepatology, imaging, and transplant setting). In clinical practice, physicians must integrate a vast array of data modalities (medical history, clinical data, laboratory tests, imaging, and pathology slides) to achieve a diagnostic or therapeutic decision. Unfortunately, machine learning and deep learning are still far from really supporting clinicians in real life. In fact, the accuracy of any technological support has no value in medicine without the support of clinicians. To make better use of new technologies, it is essential to improve clinicians' knowledge about them. To this end, the authors propose that collaborative networks for multidisciplinary approaches will improve the rapid implementation of AI systems for developing disease-customized AI-powered clinical decision support tools. The authors also discuss ethical, educational, and legal challenges that must be overcome to build robust bridges and deploy potentially effective AI in real-world clinical settings.

Delphi expert consensus for whole slide imaging in thyroid cytopathology

OBJECTIVE

Despite an increase in thyroid fine needle aspiration (FNA) and advances in whole slide imaging (WSI) adoption, digital pathology is still considered inadequate for primary diagnosis of these cases. Herein, we aim to validate the utility of WSI in thyroid FNAs employing the Delphi method strategy.

METHODS

A panel of experts from seven reference cytology centres was recruited. The study consisted of two consecutive rounds: (1) an open-ended, free-response questionnaire generating a list of survey items; and (2) a consensus analysis of 80 selected shared WSIs from 80 cases by six investigators answering six morphological questions utilising a 1 to 5 Likert scale.

RESULTS

High consensus was achieved for all parameters, with an overall average score of 4.27. The broad majority of items (84%) were ranked either 4 or 5 by each physician. Two badly scanned cases were responsible for more than half of the low-ranked (≤2) values (57%). Good to excellent (≥3) diagnostic confidence was reached in more than 95.2% of cases. For most cases (78%) WSI assessment was not limited by technical issues linked to the image acquisition process.

CONCLUSION

This systematic Delphi study indicates broad consensus among participating physicians on the application of DP to thyroid cytopathology, supporting expert opinion that WSI is reliable and safe for primary diagnostic purposes.

Histology for nephrology, from pre-implantation to post-transplant kidney biopsy. Lesson learned from ReBIrth (Renal BIopsy for Kidney Transplantation Therapy)

A meeting entitled Renal BIopsy for Kidney Transplantation Therapy (ReBIrth) took place on May 31st, 2022 in Bologna, Italy. The meeting drew together nephrologists, surgeons, and pathologists and recognized as experts in the field of kidney transplantation in Italy. In this paper, we present our experience working with kidney transplants in the current era of immunosuppression therapy. The primary aim is to report the histopathological characteristics of failed kidney allografts after a consensus of experts reviewed the cases on a wholeslide imaging digital platform. Regardless of the cases discussed, digital pathology was reliable in identifying all the morphological and immunohistochemical features required to improve the correct use of immunosuppressive therapy to prevent graft failure and optimize patient management.

European Society for Organ Transplantation (ESOT)-TLJ 3.0 Consensus on Histopathological Analysis of Pre-Implantation Donor Kidney Biopsy: Redefining the Role in the Process of Graft Assessment

The ESOT TLJ 3.0. consensus conference brought together leading experts in transplantation to develop evidence-based guidance on the standardization and clinical utility of pre-implantation kidney biopsy in the assessment of grafts from Expanded Criteria Donors (ECD). Seven themes were selected and underwent in-depth analysis after formulation of PICO (patient/population, intervention, comparison, outcomes) questions. After literature search, the statements for each key question were produced, rated according the GRADE approach [Quality of evidence: High (A), Moderate (B), Low (C); Strength of Recommendation: Strong (1), Weak (2)]. The statements were subsequently presented in-person at the Prague kick-off meeting, discussed and voted. After two rounds of discussion and voting, all 7 statements reached an overall agreement of 100% on the following issues: needle core/wedge/punch technique representatively [B,1], frozen/paraffin embedded section reliability [B,2], experienced/non-experienced on-call renal pathologist reproducibility/accuracy of the histological report [A,1], glomerulosclerosis/other parameters reproducibility [C,2], digital pathology/light microscopy in the measurement of histological variables [A,1], special stainings/Haematoxylin and Eosin alone comparison [A,1], glomerulosclerosis reliability versus other histological parameters to predict the graft survival, graft function, primary non-function [B,1]. This methodology has allowed to reach a full consensus among European experts on important technical topics regarding pre-implantation biopsy in the ECD graft assessment.

Efficacy of PD-1/PD-L1 inhibitors in advanced gastroesophageal cancer based on characteristics: a meta-analysis

Objective: Evaluate the potency of anti-PD-1/PD-L1 antibodies in advanced gastroesophageal cancer patients with different clinical features. Methods: Randomized, controlled trials comparing anti-PD-1/PD-L1 antibodies with chemotherapy in individuals with gastroesophageal cancer were retrieved. Results: 15 trials involving 9194 individuals were included. PD-1/PD-L1 inhibitors significantly improved overall survival (OS) but not progression-free survival. Significantly improved OS was observed in PD-L1 combined positive score ≥1, primary esophageal cancer, primary gastric cancer and Asian patients. Subgroup analysis revealed significant OS benefit achieved for esophageal squamous cell carcinoma, but not for esophageal adenocarcinoma. Conclusion: PD-1/PD-L1 inhibitors improved OS in advanced gastroesophageal carcinoma, especially in patients with esophageal cancer. Race, primary tumor sites and PD-L1 combined positive score can be used to predict the potency of immune checkpoint inhibitors.

Development and validation of urinary exosomal microRNA biomarkers for the diagnosis of acute rejection in kidney transplant recipients

Introduction

Acute rejection (AR) continues to be a significant obstacle for short- and long-term graft survival in kidney transplant recipients. Herein, we aimed to examine urinary exosomal microRNAs with the objective of identifying novel biomarkers of AR.

Materials and methods

Candidate microRNAs were selected using NanoString-based urinary exosomal microRNA profiling, meta-analysis of web-based, public microRNA database, and literature review. The expression levels of these selected microRNAs were measured in the urinary exosomes of 108 recipients of the discovery cohort using quantitative real-time polymerase chain reaction (qPCR). Based on the differential microRNA expressions, AR signatures were generated, and their diagnostic powers were determined by assessing the urinary exosomes of 260 recipients in an independent validation cohort.

Results

We identified 29 urinary exosomal microRNAs as candidate biomarkers of AR, of which 7 microRNAs were differentially expressed in recipients with AR, as confirmed by qPCR analysis. A three-microRNA AR signature, composed of hsa-miR-21-5p, hsa-miR-31-5p, and hsa-miR-4532, could discriminate recipients with AR from those maintaining stable graft function (area under the curve [AUC] = 0.85). This signature exhibited a fair discriminative power in the identification of AR in the validation cohort (AUC = 0.77).

Conclusion

We have successfully demonstrated that urinary exosomal microRNA signatures may form potential biomarkers for the diagnosis of AR in kidney transplantation recipients.

Value of Artificial Intelligence in Evaluating Lymph Node Metastases

One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.

Digital Slide and Simulation-Based Learning in Pre-Implantation Kidney Biopsies

Although controversial, procurement kidney biopsies and histology are commonly used in kidney allocation from deceased donors. The long series of models developed for this question, incorporating a variety of clinical and histologic variables, failed to properly predict the long-term graft survival. This failure could be explained by many factors, including the lack of expertise in terms of skilled available nephropathologists in the urgent setting of biopsies assessment. Simulation-based learning is a form of experiential learning that provides learners with a real-world-like opportunity to develop and practice their knowledge and skills but in a simulated environment. Digital pathology with whole-slide imaging is a powerful tool for knowledge delivering, as it offers the opportunity to facilitate meeting of general pathologists with experts, with availability of second opinion consultation and tailored training on specific cases. In the back of these considerations, we report on the content of the web-meeting “Digital slide and simulation-based learning in pre-implantation kidney” which was fully dedicated to the evaluation of pre-implantation kidney biopsy, with a very practical approach and a direct interaction between two expert renal transplant pathologists and the audience of general pathologists.

Perspective of a Pathologist on Benchmark Strategies for Artificial Intelligence Development in Organ Transplantation

Transplant pathology of donors is a highly specialized field comprising both the evaluation of organ donor biopsy for the oncological risk transmission and to guide the organ allocation. Timing is critical in transplant procurement since organs must be recovered as soon as possible to ensure the best possible outcome for the recipient. To all this is added the fact that the evaluation of a donor causes difficulties in many cases and the impact of these assessments is paramount, considering the possible recovery of organs that would have been erroneously discarded or, conversely, the possibly correct discarding of donors with unacceptable risk profiles. In transplant pathology histology is still the gold standard for diagnosis dictating the subsequent decisions and course of clinical care. Digital pathology has played an important role in accelerating healthcare progression and nowadays artificial intelligence powered computational pathology can effectively improve diagnostic needs, supporting the quality and safety of the process. Mapping the shape of the journey would suggest a progressive approach from supervised to semi/unsupervised models, which would involve training these models directly for clinical endpoints. In machine learning, this generally delivers better performance, compensating for a potential lack in interpretability. With planning and enough confidence in the performance of learning-based methods from digital pathology and artificial intelligence, there is great potential to augment the diagnostic quality and correlation with clinical endpoints. This may improve the donor pool and vastly reduce diagnostic and prognostic errors that are known but currently are unavoidable in transplant donor pathology.

Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta

Digital imaging, including the use of artificial intelligence, has been increasingly applied to investigate the placenta and its related pathology. However, there has been no comprehensive review of this body of work to date. The aim of this study was to therefore review the literature regarding digital pathology of the placenta. A systematic literature search was conducted in several electronic databases. Studies involving the application of digital imaging and artificial intelligence techniques to human placental samples were retrieved and analyzed. Relevant articles were categorized by digital image technique and their relevance to studying normal and diseased placenta. Of 2008 retrieved articles, 279 were included. Digital imaging research related to the placenta was often coupled with immunohistochemistry, confocal microscopy, 3D reconstruction, and/or deep learning algorithms. By significantly increasing pathologists' ability to recognize potentially prognostic relevant features and by lessening inter-observer variability, published data overall indicate that the application of digital pathology to placental and perinatal diseases, along with clinical and radiology correlation, has great potential to improve fetal and maternal health care including the selection of targeted therapy in high-risk pregnancy.

Pre-Implantation Kidney Biopsies in Extended Criteria Donors: From On Call to Expert Pathologist, from Conventional Microscope to Digital Pathology

The number of patients awaiting a kidney transplant is constantly rising but lack of organs leads kidneys from extended criteria donors (ECD) to be used to increase the donor pool. Pre-transplant biopsies are routinely evaluated through the Karpinski-Remuzzi score but consensus on its correlation with graft survival is controversial. This study aims to test a new diagnostic model relying on digital pathology to evaluate pre-transplant biopsies and to correlate it with graft outcomes. Pre-transplant biopsies from 78 ECD utilized as single kidney transplantation were scanned, converted to whole-slide images (WSIs), and reassessed by two expert nephropathologists using the Remuzzi-Karpinski score. The correlation between graft survival at 36 months median follow-up and parameters assigned by either WSI or glass slide score (GSL) by on-call pathologists was evaluated, as well as the agreement between the GSL and the WSIs score. No relation was found between the GSL assessed by on-call pathologists and graft survival (P = 0.413). Conversely, the WSI score assigned by the two nephropathologists strongly correlated with graft loss probability, as confirmed by the ROC curves analysis (DeLong test P = 0.046). Digital pathology allows to share expertise in the transplant urgent setting, ensuring higher accuracy and favoring standardization of the process. Its employment may significantly increase the predictive capability of the pre-transplant biopsy evaluation for ECD, improving the quality of allocation and patient safety.

Digital pathology world tour

Objective

Digital pathology (DP) is currently in the spotlight and is rapidly gaining ground, even though the history of this field spans decades. Despite great technological progress, the adoption of DP for routine clinical diagnostic use remains limited.

Methods

A systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all published studies that encompassed any application of DP.

Results

Of 4888 articles retrieved, 4041 were included. Relevant articles were categorized as "diagnostic" (147/4041, 4%) where DP was utilized for routine diagnostic workflow and "non-diagnostic" (3894/4041, 96%) for all other applications. The "non-diagnostic" articles were further categorized according to DP application including "artificial intelligence" (33%), "education" (5%), "narrative" (17%) for reviews and editorials, and "technical" (45%) for pure research publications.

Conclusion

This manuscript provided temporal and geographical insight into the global adoption of DP by analyzing the published scientific literature.

Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules

Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification of normal and abnormal renal tubules remain scarce. Using a deep learning-based method, we aimed to classify normal and abnormal renal tubules, thereby assisting renal pathologists in the evaluation of renal biopsy specimens. Consequently, we developed a U-Net-based segmentation model using randomly selected regions obtained from 21 renal biopsy specimens. Further, we verified its performance in multiclass segmentation by calculating the Dice coefficients (DCs). We used 15 cases of tubulointerstitial nephritis to assess its applicability in aiding routine diagnoses conducted by renal pathologists and calculated the agreement ratio between diagnoses conducted by two renal pathologists and the time taken for evaluation. We also determined whether such diagnoses were improved when the output of segmentation was considered. The glomeruli and interstitium had the highest DCs, whereas the normal and abnormal renal tubules had intermediate DCs. Following the detailed evaluation of the tubulointerstitial compartments, the proximal, distal, atrophied, and degenerated tubules had intermediate DCs, whereas the arteries and inflamed tubules had low DCs. The annotation and output areas involving normal and abnormal tubules were strongly correlated in each class. The pathological concordance for the glomerular count, t, ct, and ci scores of the Banff classification of renal allograft pathology remained high with or without the segmented images. However, in terms of time consumption, the quantitative assessment of tubulitis, tubular atrophy, degenerated tubules, and the interstitium was improved significantly when renal pathologists considered the segmentation output. Deep learning algorithms can assist renal pathologists in the classification of normal and abnormal tubules in renal biopsy specimens, thereby facilitating the enhancement of renal pathology and ensuring appropriate clinical decisions.

Technical and Diagnostic Issues in Whole Slide Imaging Published Validation Studies

Objective

Digital pathology with whole-slide imaging (WSI) has many potential clinical and non-clinical applications. In the past two decades, despite significant advances in WSI technology adoption remains slow for primary diagnosis. The aim of this study was to identify common pitfalls of WSI reported in validation studies and offer measures to overcome these challenges.

Methods

A systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all validation studies designed to evaluate the feasibility of WSI for diagnostic clinical use in pathology. Technical and diagnostic problems encountered with WSI in these studies were recorded.

Results

A total of 45 studies were identified in which technical issues were reported in 15 (33%), diagnostic issues in 8 (18%), and 22 (49%) reported both. Key technical problems encompassed slide scan failure, prolonged time for pathologists to review cases, and a need for higher image resolution. Diagnostic challenges encountered were concerned with grading dysplasia, reliable assessment of mitoses, identification of microorganisms, and clearly defining the invasive front of tumors.

Conclusion

Despite technical advances with WSI technology, some critical concerns remain that need to be addressed to ensure trustworthy clinical diagnostic use. More focus on the quality of the pre-scanning phase and training of pathologists could help reduce the negative impact of WSI technical difficulties. WSI also seems to exacerbate specific diagnostic tasks that are already challenging among pathologists even when examining glass slides with conventional light microscopy.

Computer-assisted evaluation enhances the quantification of interstitial fibrosis in renal implantation biopsies, measures differences between frozen and paraffin sections, and predicts delayed graft function

Background

(Pre-)Implantation biopsies provide important data on the quality of donor kidneys. Interstitial fibrosis, as a known predictor for kidney disease progression, is an essential feature of this evaluation. However, the assessment of frozen sections of implantation biopsies is challenging and can result in the disposal of candidate organs. We sought to apply digital image analysis (DIA) to quantify the differences between frozen and paraffin sections when evaluating interstitial fibrosis, identify factors that influence these variations and test the predictive value of the computerised measures.

Methods

We quantified the differences between frozen and paraffin sections in the same biopsy samples by measuring Sirius red-stained interstitial areas (SRIA) in DIA. We compared them to the original reports, and retrospectively correlated our findings to clinical data, graft function and outcome in 73 patients.

Results

Frozen sections display a broader interstitial area than paraffin sections, in some cases up to one-third more (mean difference + 7.8%, range − 7 to 29%). No donor-related factors (age or gender, cold ischemia time, or non-heart-beating donor) influenced significantly this difference. Compared to the original assessment of frozen vs paraffin sections in optical microscopy, the DIA of interstitial fibrosis shows a higher consistency (ICC 0.69). Our approach further allows to distinguish SRIA in paraffin sections as an independent predictor for delayed graft function (OR = 1.1; p = 0.028).

Conclusions

DIA is superior to and more consistent than routine optic microscopy for interstitial fibrosis evaluation. This method could improve implantation biopsy diagnostics and help to reduce disposal of organs.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40620-022-01315-y.

Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review

BACKGROUND

Transplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ allocation and post-transplant graft biopsy for assessment of rejection or graft damage. The introduction of digital pathology with whole-slide imaging (WSI) in clinical research, trials and practice has catalyzed the application of artificial intelligence (AI) for histopathology, with development of novel machine-learning models for tissue interrogation and discovery. We aimed to review the literature for studies specifically applying AI algorithms to WSI-digitized pre-implantation kidney biopsy.

METHODS

A systematic search was carried out in the electronic databases PubMed-MEDLINE and Embase until 25th September, 2021 with a combination of the key terms "kidney", "biopsy", "transplantation" and "artificial intelligence" and their aliases. Studies dealing with the application of AI algorithms coupled with WSI in pre-implantation kidney biopsies were included. The main theme addressed was detection and quantification of tissue components. Extracted data were: author, year and country of the study, type of biopsy features investigated, number of cases, type of algorithm deployed, main results of the study in terms of diagnostic outcome, and the main limitations of the study.

RESULTS

Of 5761 retrieved articles, 7 met our inclusion criteria. All studies focused largely on AI-based detection and classification of glomerular structures and to a lesser extent on tubular and vascular structures. Performance of AI algorithms was excellent and promising.

CONCLUSION

All studies highlighted the importance of expert pathologist annotation to reliably train models and the need to acknowledge clinical nuances of the pre-implantation setting. Close cooperation between computer scientists and practicing as well as expert renal pathologists is needed, helping to refine the performance of AI-based models for routine pre-implantation kidney biopsy clinical practice.

An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities

BACKGROUND

Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features).

MATERIALS AND METHODS

As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated.

RESULTS

Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics.

CONCLUSION

To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.

Digital pathology all stars

Digital pathology plays an important role in accelerating the progression of healthcare and the potential benefits of adopting digital technologies have been solidly established. Despite this, real-world data suggest that a fully digital approach to the histological workflow has been implemented in a minority only of pathology laboratories. The e-learning event “Digital Pathology All Stars” was conceived by the University and Hospital Trust of Verona and comprised traditional lectures made by well-recognized experts in Digital Pathology from all over the world. The meeting aimed to promote the exchange of knowledge to support and strengthen digital pathology adoption and implementation.

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.

Validation of portable tablets for transplant pathology diagnosis according to the College of American Pathologists’ Guidelines

Despite increased use of digital pathology, its application in the transplantation setting remains limited. One of the restraints is related to concerns that this technology is inadequate for supporting diagnostic work. In this study, we sought to establish non inferiority of whole slide imaging (WSI) to light microscopy (LM) for intraoperative transplantation diagnosis using inexpensive portable devices. A validation study was conducted according to updated guidelines from the College of American Pathologists (CAP) utilizing 80 intraoperative transplantation cases. Two pathologists reviewed glass slides with LM and digital slides on two different tablets after a washout period of 4 weeks. Diagnostic concordance and intra-observer agreement were recorded. A total of 45 (56%) cases were suitable for rendering transplant diagnoses and 35 (44%) for assessing cancer risk. Intra-observer agreement was 95.1% for organ suitability and 100% for cancer risk. There were no major discordances that could affect patient transplant management. Digital evaluation of intraoperative transplant specimens using tablets to view whole slide images was non-inferior to LM for primary diagnosis. This suggests that after validating WSI these digital tools can be safely used for remote intraoperative transplantation diagnostic work.

Advantages of Using a Web-based Digital Platform for Kidney Preimplantation Biopsies

BACKGROUND

In the setting of kidney transplantation, histopathology of kidney biopsies is a key element in the organ assessment and allocation. Despite the broad diffusion of the Remuzzi-Karpinski score on preimplantation kidney biopsies, scientific evidence of its correlation to the transplantation outcome is controversial. The main issues affecting the prognostic value of histopathology are the referral to general on-call pathologists and the semiquantitative feature of the score, which can raise issues of interpretation. Digital pathology has shown very reliable and effective in the oncological diagnosis and treatment; however, the spread of such technologies is lagging behind in the field of transplantation. The aim of our study was to create a digital online platform where whole-slide images (WSI) of preimplantation kidney biopsies could be uploaded and stored.

METHODS

We included 210 kidney biopsies collected between January 2015 and December 2019 from the joint collaboration of the transplantation centers of Padua and Verona. The selected slides, stained with hematoxylin and eosin, were digitized and uploaded on a shared web platform. For each case, the on-call pathologists' Remuzzi grades were obtained from the original report, together with the clinical data and the posttransplantation follow-up.

RESULTS

The storage of WSI of preimplantation kidney biopsies would have several clinical, scientific, and educational advantages. The clinical utility relies on the possibility to consult online expert pathologists and real-time quality checks of diagnosis. From the perspective of follow-up, the archived digitized biopsies can offer a useful comparison to posttransplantation biopsies. In addition, the digital online platform is a precious tool for multidisciplinary meetings aimed both at the clinical discussion and at the design of research projects. Furthermore, this archive of readily available WSI is an important educational resource for the training of professionals.

CONCLUSIONS

Finally, the web platform lays the foundation for the introduction of artificial intelligence in the field of transplantation that would help create new diagnostic algorithms and tools with the final aim of increasing the precision of organ assessment and its predictive value for transplant outcome.

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.

Cancer recognition of artificial intelligence

The recognition mechanism of artificial intelligence (AI) is an interesting topic in understanding AI neural networks and their application in therapeutics. A number of multilayered neural networks can recognize cancer through deep learning. It would be interesting to think about whether human insights and AI attention are associated with each other or should be translated, which is one of the main points in this editorial. The automatic detection of cancer with computer-aided diagnosis is being applied in the clinic and should be improved with feature mapping in neural networks. The subtypes and stages of cancer, in terms of progression and metastasis, should be classified with AI for optimized therapeutics. The determination of training and test data during learning and selection of appropriate AI models will be essential for therapeutic applications.

Pathologist Concordance for Ovarian Carcinoma Subtype Classification and Identification of Relevant Histologic Features Using Microscope and Whole Slide Imaging: A Multisite Observer Study

CONTEXT.—

Despite several studies focusing on the validation of whole slide imaging (WSI) across organ systems or subspecialties, the use of WSI for specific primary diagnosis tasks has been underexamined.

OBJECTIVE.—

To assess pathologist performance for the histologic subtyping of individual sections of ovarian carcinomas using the light microscope and WSI.

DESIGN.—

A panel of 3 experienced gynecologic pathologists provided reference subtype diagnoses for 212 histologic sections from 109 ovarian carcinomas based on optical microscopy review. Two additional attending pathologists provided diagnoses and also identified the presence of a set of 8 histologic features important for ovarian tumor subtyping. Two experienced gynecologic pathologists and 2 fellows reviewed the corresponding WSI images for subtype classification and feature identification.

RESULTS.—

Across pathologists specialized in gynecologic pathology, concordance with the reference diagnosis for the 5 major ovarian carcinoma subtypes was significantly higher for a pathologist reading on microscope than each of 2 pathologists reading on WSI. Differences were primarily due to more frequent classification of mucinous carcinomas as endometrioid with WSI. Pathologists had generally low agreement in identifying histologic features important to ovarian tumor subtype classification, with either optical microscopy or WSI. This result suggests the need for refined histologic criteria for identifying such features. Interobserver agreement was particularly low for identifying intracytoplasmic mucin with WSI. Inconsistencies in evaluating nuclear atypia and mitoses with WSI were also observed.

CONCLUSIONS.—

Further research is needed to specify the reasons for these diagnostic challenges and to inform users and manufacturers of WSI technology.

Digital pathology for second opinion consultation and donor assessment during organ procurement: Review of the literature and guidance for deployment in transplant practice

Telepathology has been an important application for second opinion consultation ever since the introduction of digital pathology. However, little is known regarding teleconsultation for second opinion in transplantation. There is also limited literature on telepathology during organ donor procurement, typically utilized when general pathologists on-call request back-up to help assess donor biopsies for organ suitability or to diagnose newly discovered tumors with urgent time constraints. In this review, we searched Pubmed/Embase and websites of transplant organizations to collect and analyze published evidence on teleconsultation for donor evaluation and organ procurement. Of 2725 records retrieved using the key terms 'telepathology', 'second opinion' and 'transplantation', 26 suitable studies were included. Most records were from North America and included validation studies of telepathology being used for remote frozen section interpretation of donor biopsies with whole slide imaging. The data from these published studies supports the transition towards digital teleconsultation in transplant settings where consultations among pathologists are still handled by pathologists being called on site, via telephone and/or email.

The Landscape of Digital Pathology in Transplantation: From the Beginning to the Virtual E-Slide

Background

Digital pathology has progressed over the last two decades, with many clinical and nonclinical applications. Transplantation pathology is a highly specialized field in which the majority of practicing pathologists do not have sufficient expertise to handle critical needs. In this context, digital pathology has proven to be useful as it allows for timely access to expert second-opinion teleconsultation. The aim of this study was to review the experience of the application of digital pathology to the field of transplantation.

Methods

Papers on this topic were retrieved using PubMed as a search engine. Inclusion criteria were the presence of transplantation setting and the use of any type of digital image with or without the use of image analysis tools; the search was restricted to English language papers published in the 25 years until December 31, 2018.

Results

Literature regarding digital transplant pathology is mostly about the digital interpretation of posttransplant biopsies (75 vs. 19), with 15/75 (20%) articles focusing on agreement/reproducibility. Several papers concentrated on the correlation between biopsy features assessed by digital image analysis (DIA) and clinical outcome (45/75, 60%). Whole-slide imaging (WSI) only appeared in recent publications, starting from 2011 (13/75, 17.3%). Papers dealing with preimplantation biopsy are less numerous, the majority (13/19, 68.4%) of which focus on diagnostic agreement between digital microscopy and light microscopy (LM), with WSI technology being used in only a small quota of papers (4/19, 21.1%).

Conclusions

Overall, published studies show good concordance between digital microscopy and LM modalities for diagnosis. DIA has the potential to increase diagnostic reproducibility and facilitate the identification and quantification of histological parameters. Thus, with advancing technology such as faster scanning times, better image resolution, and novel image algorithms, it is likely that WSI will eventually replace LM.

Hard-to-place kidney offers: Donor- and system-level predictors of discard

Understanding risk factors for deceased-donor kidney nontransplantation is important since discard rates remain high. We analyzed DonorNet^® data of consecutive deceased-donor nonmandatory share primary kidney-only offers to adult candidates at our center and beyond between July 1, 2015 and March 31, 2016 for donor- and system-level risk factors of discard, defined as nontransplantation at our or subsequent transplant centers. Exclusions were hepatitis C virus/hepatitis B virus core antibody status, blood type AB, and donor <1 year based on low candidate waitlist size. Of 456 individual kidney offers, from 296 donors, 73% were discarded. Most were national (93%) offers from Kidney Donor Profile Index 35-85% (n = 233) or >85% (n = 208) donors late in the allocation sequence with prior refusals logged for numerous candidates. On multivariate regression, factors significantly associated with discard were donor cerebrovascular accident (adjusted odds ratio [aOR]: 3.32), cancer transmission concern (aOR: 6.5), renal artery luminal compromise (aOR: 3.97), biopsy score ≥3 (aOR: 5.09), 2-hour pump resistive index >0.4 (aOR: 3.27), absence of pump (aOR: 2.58), nonspecific kidney abnormality (aOR: 2.76), increasing offer cold ischemia time category 11-15, 16-20, and >21 hours (aOR: 2.07, 2.33, 2.82), nighttime notification (aOR: 2.19), and neither kidney placed at time of offer (aOR: 2.74). Many traditional determinants of discard lack discriminatory value when granular factors are assessed. System-level factors also influence discard and warrant further study.

Digital pathology in nephrology clinical trials, research, and pathology practice

PURPOSE OF REVIEW

In this review, we will discuss (i) how the recent advancements in digital technology and computational engineering are currently applied to nephropathology in the setting of clinical research, trials, and practice; (ii) the benefits of the new digital environment; (iii) how recognizing its challenges provides opportunities for transformation; and (iv) nephropathology in the upcoming era of kidney precision and predictive medicine.

RECENT FINDINGS

Recent studies highlighted how new standardized protocols facilitate the harmonization of digital pathology database infrastructure and morphologic, morphometric, and computer-aided quantitative analyses. Digital pathology enables robust protocols for clinical trials and research, with the potential to identify previously underused or unrecognized clinically useful parameters. The integration of digital pathology with molecular signatures is leading the way to establishing clinically relevant morpho-omic taxonomies of renal diseases.

SUMMARY

The introduction of digital pathology in clinical research and trials, and the progressive implementation of the modern software ecosystem, opens opportunities for the development of new predictive diagnostic paradigms and computer-aided algorithms, transforming the practice of renal disease into a modern computational science.

Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology: A Multicenter Blinded Randomized Noninferiority Study of 1992 Cases (Pivotal Study)

Most prior studies of primary diagnosis in surgical pathology using whole slide imaging (WSI) versus microscopy have focused on specific organ systems or included relatively few cases. The objective of this study was to demonstrate that WSI is noninferior to microscopy for primary diagnosis in surgical pathology. A blinded randomized noninferiority study was conducted across the entire range of surgical pathology cases (biopsies and resections, including hematoxylin and eosin, immunohistochemistry, and special stains) from 4 institutions using the original sign-out diagnosis (baseline diagnosis) as the reference standard. Cases were scanned, converted to WSI and randomized. Sixteen pathologists interpreted cases by microscopy or WSI, followed by a wash-out period of ≥4 weeks, after which cases were read by the same observers using the other modality. Major discordances were identified by an adjudication panel, and the differences between major discordance rates for both microscopy (against the reference standard) and WSI (against the reference standard) were calculated. A total of 1992 cases were included, resulting in 15,925 reads. The major discordance rate with the reference standard diagnosis was 4.9% for WSI and 4.6% for microscopy. The difference between major discordance rates for microscopy and WSI was 0.4% (95% confidence interval, -0.30% to 1.01%). The difference in major discordance rates for WSI and microscopy was highest in endocrine pathology (1.8%), neoplastic kidney pathology (1.5%), urinary bladder pathology (1.3%), and gynecologic pathology (1.2%). Detailed analysis of these cases revealed no instances where interpretation by WSI was consistently inaccurate compared with microscopy for multiple observers. We conclude that WSI is noninferior to microscopy for primary diagnosis in surgical pathology, including biopsies and resections stained with hematoxylin and eosin, immunohistochemistry and special stains. This conclusion is valid across a wide variety of organ systems and specimen types.

Mobile Health in Solid Organ Transplant: The Time Is Now

Despite being in existence for >40 years, the application of telemedicine has lagged significantly in comparison to its generated interest. Detractors include the immobile design of most historic telemedicine interventions and the relative lack of smartphones among the general populace. Recently, the exponential increase in smartphone ownership and familiarity have provided the potential for the development of mobile health (mHealth) interventions that can be mirrored realistically in clinical applications. Existing studies have demonstrated some potential clinical benefits of mHealth in the various phases of solid organ transplantation (SOT). Furthermore, studies in nontransplant chronic diseases may be used to guide future studies in SOT. Nevertheless, substantially more must be accomplished before mHealth becomes mainstream. Further evidence of clinical benefits and a critical need for cost-effectiveness analysis must prove its utility to patients, clinicians, hospitals, insurers, and the federal government. The SOT population is an ideal one in which to demonstrate the benefits of mHealth. In this review, the current evidence and status of mHealth in SOT is discussed, and a general path forward is presented that will allow buy-in from the health care community, insurers, and the federal government to move mHealth from research to standard care.

Macrophage density in early surveillance biopsies predicts future renal transplant function

Inflammation impairs renal allograft survival but is difficult to quantify by eye at low densities. Here we measured leukocyte abundance in early surveillance biopsies by digital image analysis to test for a role of chemokine receptor genotypes and analyze the predictive value of leukocyte subsets to allograft function. In six-week surveillance biopsies, T-cell (CD3), B-cell (CD20), macrophage (CD68), and dendritic cell (CD209) densities were assessed in whole slide scans. Renal cortical CD3, CD20, and CD68 were significantly higher in histologic rejection. The CCR2 V64I genotype was associated with lower CD3 and CD209 densities. Above-median CD68 density was significantly associated with lower combined patient and graft survival with a hazard ratio of 3.5 (95% confidence interval 1.1-11.0). Both CD20 and CD68 densities inversely correlated with estimated glomerular filtration rate (eGFR) four years after transplantation. Additionally, CD68 correlated with eGFR loss. Among histological measurements including a complete Banff classification, only CD68 density was a significant predictor of an eGFR under 30ml/min after four years (odds ratio 7.4, 1.8-31.0) and part of the best eGFR prediction set in a multivariable linear regression analysis of multiple clinical and pathologic parameters. In a second independent cohort, the original CD68 median maintained its discriminative power for survival and eGFR. Thus, digital high-resolution assessment of CD68⁺ leukocyte infiltration significantly improves prognostic value of early renal transplant biopsies.

The Empirical Foundations of Telepathology: Evidence of Feasibility and Intermediate Effects

INTRODUCTION

Telepathology evolved from video microscopy (i.e., "television microscopy") research in the early 1950s to video microscopy used in basic research in the biological sciences to a basic diagnostic tool in telemedicine clinical applications. Its genesis can be traced to pioneering feasibility studies regarding the importance of color and other image-based parameters for rendering diagnoses and a series of studies assessing concordance of virtual slide and light microscopy diagnoses. This article documents the empirical foundations of telepathology.

METHODS

A selective review of the research literature during the past decade (2005-2016) was conducted using robust research design and adequate sample size as criteria for inclusion.

CONCLUSIONS

The evidence regarding feasibility/acceptance of telepathology and related information technology applications has been well documented for several decades. The majority of evidentiary studies focused on intermediate outcomes, as indicated by comparability between telepathology and conventional light microscopy. A consistent trend of concordance between the two modalities was observed in terms of diagnostic accuracy and reliability. Additional benefits include use of telepathology and whole slide imaging for teaching, research, and outreach to resource-limited countries. Challenges still exist, however, in terms of use of telepathology as an effective diagnostic modality in clinical practice.