Literature DB >> 31367473

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

Ilaria Girolami1, Anil Parwani2, Valeria Barresi1, Stefano Marletta1, Serena Ammendola1, Lavinia Stefanizzi1, Luca Novelli3, Arrigo Capitanio4, Matteo Brunelli1, Liron Pantanowitz5, Albino Eccher1.   

Abstract

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.

Entities:  

Keywords:  Digital pathology; donor biopsy; graft biopsy; image analysis; transplantation

Year:  2019        PMID: 31367473      PMCID: PMC6639852          DOI: 10.4103/jpi.jpi_27_19

Source DB:  PubMed          Journal:  J Pathol Inform


INTRODUCTION

Digital pathology has progressed over the last two decades and is being used for several clinical and nonclinical applications. Some of these use cases, including primary diagnosis, second-opinion consultation, archiving, education/training, research, and image analysis. Many studies have been performed on the implementation and validation of digital systems. Several reviews have reported on the concordance between whole-slide imaging (WSI) and conventional light microscopy (LM) in surgical pathology[12] and highlighted some of the technical challenges related to WSI in cytology.[3] In addition, several digital image analysis (DIA) tools have been developed over the years, and apart from their role in quantitative image analysis of breast biomarkers, these algorithms have been used mainly for research purposes. Transplantation pathology is a highly specialized field in which the majority of pathologists do not have enough expertise to handle critical practice needs. Digital pathology can be extremely useful in this regard as it allows general pathologists to employ teleconsultation for intraoperative consultation as well as to rapidly gain an expert second opinion. In addition, DIA can be applied to transplant biopsies to facilitate the identification and quantification of several morphological parameters, as well as their spatial relationships. The aim of this paper was to review the literature on transplantation digital pathology published in the last 25 years and to review the main issues, results, and future directions of the field.

METHODS

Papers on this topic were retrieved using PubMed as a search engine. The search was limited to papers written in the English language and published in the 25 years' time span until December 31, 2018, with the following search strategy: “(“digital” OR “whole slide imaging” OR “WSI” OR “digital pathology” OR “telepathology” OR “telemedicine” OR “image analysis”) AND (“transplant” OR “transplantation” OR “organ” OR “organ procurement” OR “preimplantation biopsy” OR “graft” OR “allograft”) AND (“renal” OR “kidney” OR “liver” OR “heart” OR “lung” OR “pancreas“)”. Inclusion criteria were the presence in the study of the transplantation setting, pre- or post-transplant, and the use of any type of digital pathology image, both with or without the use of image analysis tools. Papers dealing with digital pathology and biopsies but not in transplantation setting, reviews, and commentaries were excluded. Papers retrieved were divided into pre- and post-transplant phase and grouped according to the organ of interest in the study, type of digital pathology, use of image analysis tools, main topic of the study among concordance/reproducibility, assessment of features for organ outcome and rejection, and other morphological or immunohistochemical (IHC) issues.

Distribution of studies

A total of 2207 papers were retrieved with the search strategy, and the main reasons for exclusion on the basis of title and abstract were (i) the absence of the transplantation setting, as the term “transplant” was intended only for tissues in plastic and reconstructive surgery; (ii) the absence of a digitalized image, as the term “digital” was intended for other imaging modalities; and (iii) the use of animal models. The included papers were 93, with the note that a single study[4] comprised both pre- and post-transplant biopsies, so it was counted in both groups. The studies included so represented about 4% of all retrieved items. There were a growing number of publications in the last 15 years as more than 75% of papers have been published after 2004. Subdividing the studies according to the type of digital pathology, it can be seen how the static image modality use has started to decrease after 2008 and how the number of publications using WSI is increasing in the last decade, overcoming the static digitized image in the most recent period 2014–2018. A graphical summary of the distribution of studies over time is shown in Figure 1. Regarding the main issues addressed in the studies, the concordance between modalities was the main topic overall in pretransplant phase papers (14/19, 73.7%), while it was the focus of the study only in 20% (15/75) of posttransplant studies. Indeed, in this group, the correlation of histological features assessed with digital instruments with outcome and the investigation of features related to rejection represented together the most common issues, with total 60% (45/75) of publications. Splitting according to technology type, it can be observed that in studies using WSI, the main topic is the concordance between WSI and conventional LM, both in pretransplant (all 4 studies) and posttransplant (9/13, 69.2%) studies. The assessment of histological features correlated to outcome of organ or with particular attention to rejection was the main topic of the studies using static digitized images (41/70, 58.6%, all posttransplant studies). A diagram of distribution of studies according to transplant phase, type of digital pathology, and main topic is shown in Figure 2.
Figure 1

Number of publications over time and according to the type of digital pathology. WSI: Whole-slide imaging, NOS: Not otherwise specified

Figure 2

Hierarchy of papers according to transplantation phase, mode of digital pathology, and main topic of study. *A paper is counted in both groups as it comprises both pre- and post-transplant biopsies. IHC: Immunohistochemistry, WSI: Whole-slide imaging

Number of publications over time and according to the type of digital pathology. WSI: Whole-slide imaging, NOS: Not otherwise specified Hierarchy of papers according to transplantation phase, mode of digital pathology, and main topic of study. *A paper is counted in both groups as it comprises both pre- and post-transplant biopsies. IHC: Immunohistochemistry, WSI: Whole-slide imaging

Modes of digital pathology

Static telepathology requires only a microscope with an attached digital camera connected to a monitor or computer, internet access, and secure sharing software. A remote expert pathologist can view these static images but relies on an on-site pathologist who controls the microscope to capture relevant images that are in focus, which makes this inexpensive system restrictive.[5] This can be overcome with robotic or dynamic telepathology, which allows the remote pathologist to control the microscope using software; however, this robotic system is more expensive, is time-consuming, and demands a high network bandwidth.[5] WSI scanners are essentially a microscope and software-driven robotic stage that methodically moves the slide in the x and y axes under the microscopic lens while simultaneously optimizing the Z-plane focus and photographing each microscopic field.[5] WSI scanners can be tile-based (the most common ones), in which a square photosensor is used to capture multiple tiles adjacent to each other, or line scan-based imaging, in which an oblong photosensor is used to continually capture strips of image data as it sweeps through the slide. The quality of focusing is limited by multiple optical and mechanical parameters, notably the numerical aperture (NA) of the objective and movement resolution on the vertical (z) axis. Higher NA allows the distance that can be resolved to become smaller, thus increasing resolution.[6] WSI has proven to be superior in comparison to conventional microscopy in terms of case organization, navigation and annotation of slide, easiness to share for consultation and multiple viewing, and to be reliable for routine surgical pathology diagnosis, after validation of systems.[7] However, scanning time at higher resolutions, storage issues, and costs remain open questions that could have limited widespread adoption of this technology at the beginning; however, nowadays, for academic institutions or community hospitals with a high diagnostic workload, these issues are not to be considered a barrier. Indeed, as reported by a recent international survey, after full implementation of digital pathology, in routine practice, the new step could be the integration of artificial intelligence tools in diagnostic pathology.[8] Finally, hybrid WSI-robotic technology offers pathologists the ability to switch between live robotic viewing and a scanned digital slide.[9] The use of WSI in the transplantation literature only appears after 2011 (13/75, 17.3% of posttransplantation and 4/19, 21.1% of pretransplantation papers).

Telepathology in transplantation

The application of telemedicine to transplantation has lagged significantly compared to other medical fields, despite widespread interest.[10] The clinical benefits of mobile health technologies have been demonstrated in various phases of organ transplantation, including adherence of patients to therapy, clinical monitoring, and increase in life quality of recipients. In addition, in recent years, a number of case series and feasibility studies have highlighted the importance of digital pathology for providing access to expert second opinions. Indeed, this technology can help with real-time allograft selection and assessment of donor/recipient tissue specimens by allowing the teleconsultation of professionals during both pre- and post-transplant phases in medical centers with minimal experience.[10] However, the working scenarios in pre- and post-transplant phases is quite different. The posttransplant phase is best handled by a dedicated subspecialized pathologist, without the need for urgent turnaround times, and if needed availability of ancillary techniques. On the other hand, preimplantation diagnosis can typically be handled by an on-call general pathologist but does need to meet a turnaround time of only a few hours and usually without the luxury of ancillary studies (i.e., diagnoses depend almost entirely on a hematoxylin and eosin stain). In both scenarios, the need for diagnostic teleconsultation may be important. The vast majority of papers on digital pathology and transplantation published in the last 25 years dealt with the posttransplant biopsy during graft surveillance (75 posttransplant vs. 19 pretransplant articles, 79.8% vs. 20.2%). Minervini et al. reported their experience with second-opinion teleconsultation using a static telepathology system between the Mediterranean Institute for Transplantation and Advanced Specialized Therapies in collaboration with the University of Pittsburgh Medical Center.[4] In that study, the authors reviewed 18 posttransplant biopsies and five preimplantation frozen section (FS) liver biopsies. They assessed the agreement rates between the referring and consulting pathologist and the reliability and easiness of telepathology for obtaining a rapid second opinion.[4] Low experience with digital pathology in the pretransplantation phase may be attributed to several reasons. Before the development of contemporary WSI scanners, the acquisition of digital images (e.g., static photographs) required a lengthy amount of time that was inconsistent with the rapid turnaround time needed for preimplantation biopsy assessment. Over time, as imaging devices began to allow dynamic and robotic telemicroscopy, so did the use of telepathology to remotely read intraoperative FSs before organ transplantation.[9]

Digital image analysis in transplantation

Although as stated in recent reviews,[1112] the risk/benefit ratio and relative value of postimplantation biopsy for graft surveillance could appear to be decreasing, compared to less invasive monitoring techniques, given the development of newer noninvasive imaging and fluid techniques. However, advances in digital imaging techniques, robotics, and computing can provide new “toolkits” enabling pathologists to gain more information from tissue samples and to increase the histopathology value.[11] Indeed, starting from the early 90s, image analysis morphometric studies have been performed mainly for the detection of signs of rejection and prediction of organ outcome. The absence of time limitation comparing to pretransplant phase allows the pathologist to use ancillary techniques, to digitalize images, and to ask for consultation and perform image analysis, after slide scanning, and take advantage of DIA techniques for precise quantification of morphological features on biopsies. Among the posttransplant studies, 58/75 (77.3%) were carried out using conventional microscopy plus DIA, 8/75 (10.7%) were performed using WSI plus DIA, while 9/75 (12%) did not use DIA techniques. As clarified by Isse et al., morphometric software programs, which can range from relatively inexpensive basic macro-driven software for color quantification, too expensive and complex, trainable model-based applications for recognizing and quantifying tissue patterns, now consider WSI.[11] Moreover, the development of multiplex staining DIA algorithms and of deep learning algorithms has been rapidly increasing in recent years, with several applications in cancer pathology, that can be also applicable to transplantation biopsy pathology.[12] Therefore, it is reasonable that in the next two decades, the proportion of WSI versus LM in image analysis studies will be reversed as more image analysis studies will use WSI and deep learning algorithms.

Digital pathology in pre-transplantation

Despite the greater number of published studies on posttransplantation biopsies, there is increasing awareness of the potential to use digital pathology in the pretransplantation phase. Pathologists involved in on-call rotations for the transplant service may be asked to classify lesions found during donor assessment and to evaluate the suitability of organs to transplant from small biopsies. For newly discovered lesions, the pathologist performing these duties needs to define their nature and exclude a malignant neoplasm that would preclude safe transplantation.[13] The studies concerning preimplantation biopsies are summarized in Table 1. Among 19 studies concerning the pretransplant phase, none addressed diagnostic issues of newly discovered lesions. However, given that these lesions are typically examined by means of FS, they are probably incorporated in other more general studies about digital pathology for intraoperative consultation. Most studies on organ assessment (14/19, 73.7%) were mainly about liver and kidney biopsy,[414151617181920212223242526] while only a small proportion (5/19, 26.3%)[2728293031] dealt with pancreatic islet preparations for transplant. With regard to the type of digital pathology technology used, 12/19 (63.2%) studies discussed DIA applied to LM-acquired images, 4/19 (21.1%) studies used WSI,[15162526] one study involved only static telepathology without DIA,[4] another referred generally to using a “virtual microscope,”[24] and one did not clarify the type of digital pathology used.[23]
Table 1

Summary of papers dealing with pre-transplantation phase

Author, yearType of digital pathologyNumber of patients/biopsiesType of biopsyInterventionControls or comparisonsOutcomes/Aim of the studyResults
Minervini etal., 2001Static102Various case types, among which 5 donor FS liver biopsiesConsultant telepathology reviewReferring pathologist original diagnosisAgreement rates, descriptive86% agreement and 14% (only 3% major) disagreement between referring and consultant pathologist
Li etal., 2002LM plus DIA102Donor kidney biopsyDIA software assessmentNoneGlomerular volume and sclerosis in different age groupsGlomerular size and global sclerosis increase with age
Benkoel etal., 2003Confocal laser microscopy plus DIA30Donor liver biopsy, preimplantation and postreperfusionDIA assessment of IHC staining for ICAM-1NoneDifference in ICAM-1 expression between preimplantation and postreperfusion biopsiesHigher expression of ICAM-1 in sinusoidal endothelial cells in postreperfusion biopsies
Benkoel etal., 2003Confocal laser microscopy plus DIA30Donor liver biopsy, preimplantation and postreperfusionDIA assessment of IHC staining for F-actinNoneDifference in F-actin expression between preimplantation and postreperfusion biopsiesSignificantly lower expression of F-actin in postreperfusion biopsies
Benkoel etal., 2003Confocal laser microscopy plus DIA30Donor liver biopsy, preimplantation and postreperfusionDIA assessment of IHC staining for NaK-ATPaseNoneDifference in NaK-ATPase expression between preimplantation and postreperfusion biopsiesSignificantly lower expression of NaK-ATPase in postreperfusion biopsies
Marsman etal., 2004LM plus DIA49Donor liver biopsy, FSDIA software assessmentPathologist with glass slidePercentage of total fat, microvesicular and macrovesicular steatosis; correlation with liver function indices, graft and patient survivalSignificant correlation between pathologist and software for macrovesicular steatosis and total fat; significant association of macrovesicular steatosis and graft survival both when assessed by pathologist or software
Niclauss etal., 2008Static, stereo- microscope plus DIA12Pancreatic islets preparationsComputerized by 2 software and manual counting on digital imagesManual counting at microscopeNumber, islet equivalents and purity of islet preparationTotal islet number, equivalents number, and purity were much better correlated between digital manual and computerized analyses than between standard manual and computerized analyses
Kissler etal., 2009LM plus DIA12Pancreatic islets preparationsComputerized by software on digital imageManual counting on digital imageAccuracy, intra- and inter-observer reproducibility for both modalities by means of CVDigital image analysis is reliable for islet counting, with the advantage of permanent records and quality assurance
Biesterfield etal., 2012Static LM, point grid counting120Donor liver biopsy, cut in half for FS and FFPEPoint grid countingConventional LMInterobserver agreement for FS and FFPE, correlation between macro- and micro-vesicular steatosisSubstantial agreement (κ>0.60) and high correlation (r>0.80) between observers and types of steatosis; no advantage for point grid analysis
Native etal., 2013LM plus DIA9 patients,54 imagesDonor liver biopsyModel-based segmentation method algorithmExpert pathologists with LMCorrelation between pathologists’ assessments and automated image analysis-based evaluations of ld-MaS percentagesNew algorithm proposed significantly improves separation between large and small macrovesicular lipid droplets (specificity 93.7%, sensibility 99.3%) and correlation with pathologists’ ld-MaS percentage assessments (r=0.97)
Gymr etal., 2015LM plus DIA42Pancreatic islets preparationsAutomated by software on digital imageManual counting at LMCorrelation of modalities for total islet number, equivalent number, and purity; intraobserver variabilityHigh correlation between modalities for total islet and equivalent number; high intraobserver reproducibility for the use of software
Wang etal., 2015LM plus DIA25 patients,84 samplesPancreatic islets preparationsComputerized by software on digital imageManual counting on digital imageCorrelation of modalities for total islet number, equivalent number, and puritySignificantly high correlation between modalities; not significant difference for total counts
Mammas etal., 2015Not clearly defined518 imagesDonor kidney, liver and pancreasDiagnosis on digital image on 4 different viewing devicesDiagnosis of reference pathologist, not stated if with LM or digitalAccuracy of diagnosis with different viewing devicesThe desktop and the experimental telemedicine platform are more reliable than tablet and mobile phone devices
Buchwald etal., 2016LM plus DIA3 patients,14 samplesPancreatic islets preparationsComputerized by software on digital imageManual counting at LMCorrelation of modalities for total islet number, equivalent number, and purity; intraobserver variabilityVery good overall correlation between modalities; lower intraobserver variability for DIA
Eccher etal., 2016WSI62 patients,124 biopsiesDonor kidney wedge biopsyPathologist with WSIPathologist with glass slideIntra- and inter-observer reproducibility with weighted Cohen k indexVery high intraobserver agreement (κ=0.961) for WSI and glass slide; slightly lower (κ=0.863) interobserver agreement for WSI than glass slide (κ=0.903)
Osband etal., 2016Virtual microscope, not otherwise specified23 kidneysDonor kidney wedge biopsy, FSExperienced pathologist with virtual microscopeOn-site pathologistTime to biopsy readShorter time to biopsy read with virtual microscope; improved time to local acceptance but not cold ischemia time or DGF rate
Liapis etal., 2017WSI40Donor kidney biopsyExperienced pathologist with WSINoneIntraclass correlation coefficient for various parameters of scoreModest agreement among pathologist, only number of glomeruli, sclerosed glomeruli and interstitial fibrosis with ICC >0.5
Cima etal., 2018WSI2816 donor kidney wedge biopsy, FS 12 donor liver biopsy, FSScoring with WSIScoring with glass slideAccuracy rate; intraobserver concordance with weighted Cohen k index; sensibility, specificity, PPV, NPV86% accuracy rate, high intraobserver concordance (κ=0.91); 96%, 75%, 96%, 75% sensibility, specificity, PPV, NPV, respectively
Marsh etal., 2018WSI17 patients,48 biopsy imagesDonor kidney biopsy, FSPatch-based model and fully convolutional model on WSIExpert pathologist scoring with WSIComparison between the two models and with pathologist’s assessment on WSI in counting total glomeruli and sclerosed glomeruliFully convolutional model substantially outperforming the model trained on image patches of isolated glomeruli, in terms of both accuracy and speed

CV: Coefficient of variation, DIA: Digital image analysis, FFPE: Formalin-fixed, paraffin-embedded, FS: Frozen section, LM: Light microscopy, ld-MaS: Large droplet Macrovesicular steatosis, NPV: Negative predictive value, PPV: Positive predictive value, WSI: Whole slide imaging, ICAM-1: Intercellular adhesion molecule-1, DGF: Delayed graft function, IHC: Immunohistochemistry, ICC: Islet cell counter

Summary of papers dealing with pre-transplantation phase CV: Coefficient of variation, DIA: Digital image analysis, FFPE: Formalin-fixed, paraffin-embedded, FS: Frozen section, LM: Light microscopy, ld-MaS: Large droplet Macrovesicular steatosis, NPV: Negative predictive value, PPV: Positive predictive value, WSI: Whole slide imaging, ICAM-1: Intercellular adhesion molecule-1, DGF: Delayed graft function, IHC: Immunohistochemistry, ICC: Islet cell counter The majority of studies (14/19, 73.7%) concerning the pretransplant phase addressed the agreement/concordance of digital pathology with the conventional LM technique. The assessment of agreement was performed with different statistical tests. Minervini et al. reported an agreement rate of 86% between referring pathologist with LM and consultant pathologist with static digital pathology, but they did not specify the agreement rates for the each of the pretransplant cases.[4] Other studies from the same group followed guidelines of the College of American Pathologists for validating WSI systems and compared WSI to LM in the assessment of kidney and liver biopsies. In one of their studies, the intraobserver concordance was excellent (κ = 0.961). The interobserver concordance was excellent for both LM (κ = 0.903) and WSI (κ = 0.863).[26] In another study on the validation of a WSI scanner, the case population included 28 scanned FS slides of the liver and kidney biopsy for organ suitability; the intraobserver concordance was excellent (κ = 0.91) with an accuracy rate of 86%.[15] Biesterfeld et al. analyzed the interobserver concordance in the quantification of macro- and micro-vesicular steatosis in liver biopsies using digital pathology. They found good interobserver agreement (κ >0.70) for all degrees of steatosis (correlation coefficient r > 0.90 and r > 0.60) when the assessment was performed with LM, but the concordance rate was lower when using point grid counting on digitized images. Therefore, they concluded that point grid counting on the digital image does not add value for steatosis quantification.[22] Two other studies analyzed the correlation between macrovesicular steatosis assessed by an experienced pathologist with LM to that assessed by DIA software (r2 = 0.426). One study reported low correlation (r2 = 0.426); however, DIA measurements had stronger correlation with liver function after transplant.[21] In the other study, a high correlation (r2 = 0.97) was found between pathologist's assessment and the DIA method.[18] Several studies concerned pancreatic islet preparations for islet transplant and compared the assessment of various parameters, including the number of islets, islet equivalents (islets normalized for an average size of 150 μm, IEQ), and purity using different methods. All of them reported high correlation between manual counting on LM[2829] or on a digitized image[30] and counting using automated/computerized DIA software (determination coefficient r2 = 0.91, r2 = 0.78 and linear coefficient r > 0.819, respectively). Three studies compared manual LM and automated DIA software by means of the coefficient of variation (CV), reporting that the CV is lower for automated software compared to manual counting[2729] and concluding that DIA is reliable for quantification of IEQ and purity.[30] Finally, one study compared three modalities (i.e., manual assessment on LM, manual assessment of digital images, and counting by DIA using software) and reported a high correlation between assessment of digital images and software analysis (r2 > 0.8) and a lower correlation between standard manual assessment and software analysis (r2 0.62–0.73).[31] Recently, some authors developed a deep learning model to identify and classify nonsclerosed and sclerosed glomeruli in WSI scans of donor kidney FS biopsies. They reported that their model based on convolutional neural networks yielded results comparable with those achieved by an expert renal pathologist, being robust enough to handle FS artifacts and adding value to the time-sensitive demand of donor biopsy evaluation. Their study is the first to specifically address glomerular recognition and classification in the FS preimplantation biopsy.[16] The Banff group analyzed reproducibility among pathologists using WSI slides in a population of 40 donor kidney biopsies, with a different proportion of core versus wedge biopsies and FS versus paraffin technique. They reported overall good-to-excellent reproducibility for counting the total number of glomeruli, for assessing the percentage of sclerosed glomeruli and number of sclerosed glomeruli and interstitial fibrosis; however, the interobserver concordance was fair to poor in the assessment of other parameters.[25] Osband et al. compared the time-to-donor kidney biopsy result between virtual microscopy and standard LM in practice and demonstrated a significant reduction in time-to-biopsy result using digital microscopy.[24] Mammas et al. compared the accuracy rate for the diagnosis of kidney, liver, and pancreas biopsies with a pathologist reading a digital slide on different devices, and they demonstrated that mobile phones and tablets to be less reliable than desktop viewing.[23] Finally, Benkoël et al. examined the expression of different IHC markers in a subset of paired preimplantation and postreperfusion liver biopsies, using DIA of confocal laser scanning microscope images, without comparison to conventional LM IHC.[141920]

Digital pathology in post-transplantation

Among the 75 retrieved studies on posttransplant biopsies, 10 (13.5%) were concerned with liver biopsy, 16 (21.6%) with the heart and lung, and 47 (63.5%) kidney.

Liver graft biopsy

The studies concerning posttransplant liver graft biopsies are summarized in Table 2. Two studies[432] described the agreement with digital static pathology diagnosis and reported high concordance rates. Two more recent studies explored the reliability of WSI slides when compared to LM or reference diagnosis.[3334] In the study by Neil et al., pathologists at several centers scored C4d antibody expression in liver biopsy tissue microarrays using WSI and LM. Interobserver agreement was variable with WSI when considering the different compartments of staining in a liver biopsy; in particular, concordance was good for the assessment of portal vein, central vein, and portal capillary compartments (κ = 0.60–0.80) and fair in the evaluation of sinusoidal and hepatic artery endothelium compartments (κ = 0.30–0.40). There was substantial agreement between pathologists with WSI and glass slides although κ indexes were not reported.[33] In the study of Saco et al., where WSI and LM were compared, the authors reported excellent intra- and inter-observer agreement (κ = 0.80–0.90) between modalities. Moreover, the authors highlighted the advantage of using WSI for viewing multiple slides, which is important because, in liver graft pathology, several stains are often used.[34]
Table 2

Summary of papers dealing with posttransplantation liver graft biopsy

Author, yearType of digital pathologyNumber of patients/biopsiesType of biopsyInterventionControls or comparisonsOutcomes/Aim of studyResults
Ito etal., 1994Static22Graft liver and kidney biopsyTelepathology diagnosisDirect LM diagnosisDescriptive resultsAgreement in 10/12 kidney biopsies and in 9/10 liver biopsies
Ben-Hari etal., 1995LM plus DIA55 (92 biopsies)Graft liver biopsyDIA assessment of eosinophil count, cell density and cross-sectional area in portal tractNoneDescriptive correlation of parameters with different degrees of rejectionPositive correlation of all parameters with severity of rejection
Minervini etal., 2001Static102, among which 9 liver graft and 9 kidney graft biopsiesVarious case types: Second opinion consultation, transplantation pathology, general surgical pathologyConsultant telepathology reviewReferring pathologist original diagnosisAgreement rates, descriptive86% agreement and 14% (only 3% major) disagreement between referring and consultant pathologist
El-Refaie etal., 2005LM plus DIA267 (343 biopsies)Graft liver biopsyDIA software quantification of mast cells and IHC stainingNoneCorrelation of mast cell count and IHC staining with different degrees of rejectionStrong correlation of mast cells with acute rejection and of IHC staining for c-Kit with severity of rejection
Calvaruso etal., 2008LM plus DIA115 (225 biopsies)Graft liver biopsyDIA software quantification of collagen proportionate areaNoneDescriptive correlation between DIA measurements, Ishak score, and portal hypertensionCollagen proportionate area assessed by DIA correlated with Ishak stage scores and portal hypertension
Guzman etal., 2010LM plus DIA19 (33 biopsies)Graft liver biopsyAnisonucleosis and oxidative damage scored by DIANoneDescriptive correlation of anisonucleosis with different clinical parametersHigher anisonucleosis in individuals with diabetes and with high expression of oxidative damage marker
Manousou etal., 2011LM plus DIA135Graft liver biopsyComputer-assisted DIA quantification of collagen proportionate areaNoneDescriptive correlation between DIA measurements, Ishak score, and decompensationCollagen proportionate area assessed by DIA correlated with Ishak stage scores and decompensation
Calvaruso etal., 2012LM plus DIA65Graft liver biopsyComputer-assisted DIA quantification of collagen proportionate areaNoneDescriptive correlation between DIA measurements, portal hypertension and graft outcomeCollagen proportionate area assessed by DIA correlated with portal hypertension and decompensation
Manousou etal., 2013LM plus DIA155 (587 biopsies)Graft liver biopsyComputer-assisted DIA quantification of collagen proportionate area and rate of increaseNoneDescriptive correlation of DIA measurements and Ishak score with portal hypertension and graft outcomeProgression rate of fibrosis is a better predictor of clinical outcome than progression by Ishak stage
Sclair etal., 2016LM plus DIA60Graft liver biopsyDIA software assessment of ductular reaction in HCV recurrent recipients with cirrhosisDIA software assessment of ductular reaction in stable recurrent HCV recipients with no cirrhosis or fibrosing hepatitisDescriptive difference among the groupsSignificantly higher ductular reaction in recipients with cirrhosis
Neil etal., 2017WSI40TMAs of graft and native liver, kidney, heartPathologists scoring C4d with WSIPathologists scoring C4d with LMDescriptive surveys of pathologists and comparison of staining methodsStrong and diffuse portal vein and capillary C4d staining, determined by both local and central pathologists, distinguished acute antibody-mediated rejection from native livers
Saco etal., 2017WSI64Graft liver biopsyPathologist with WSIPathologist with LMIntra- and inter-observer agreementAlmost perfect intraobserver concordance between modalities; high interobserver concordance for WSI (κ=0.80)

DIA: Digital image analysis, HCV: Hepatitis C virus, IHC: Immunohistochemistry, LM: Light microscopy, TMAs: Tissue microarrays, WSI: Whole-slide imaging

Summary of papers dealing with posttransplantation liver graft biopsy DIA: Digital image analysis, HCV: Hepatitis C virus, IHC: Immunohistochemistry, LM: Light microscopy, TMAs: Tissue microarrays, WSI: Whole-slide imaging Other studies regarding liver biopsy focused on the correlation with clinical parameters and predictive value on organ outcome for several features assessed by DIA software, such as fibrosis determined as collagen proportionate area (CPA) with Sirius red stain,[35363738] ductular reaction assessed with CK7 staining,[39] nuclear size, and IHC markers of oxidative damage.[40] In particular, CPA assessed as a continuous measure with DIA is reported to be a better predictor of graft outcome than Ishak stage assessed on conventional LM.[35363738] Ductular reaction area assessed with DIA software is reported to correlate with hepatic progenitor cell number assessed by manual counting and to be associated with hepatitis C virus (HCV) recurrence.[39] Nuclear size and anisonucleosis quantified with DIA software were not associated with any clinical parameters, except diabetes and the presence of a marker of oxidative damage.[40] Two older studies investigated the presence and role of overall inflammatory cells[41] and mast cells[42] for acute and chronic rejection, with quantification of cellular infiltrates or specific subtypes of mast cells with DIA software in digital images; they showed that the number of inflammatory cells assessed by DIA was able to separate mild from severe rejection[41] and that mast cell density both with tryptase and c-Kit staining correlated with the severity of acute and chronic rejection.[42]

Heart and lung graft biopsy

The studies concerning posttransplant heart and lung graft biopsies are summarized in Table 3. Of papers concerning heart and lung graft biopsy, 2/16 (12.5%) dealt with agreement and reproducibility between digital slides and LM. The oldest study by Marchevsky et al. reported concordance rates of 96% and 82.8% with Cohen's κ coefficients of 0.92 and 0.692 for lung and heart biopsy, respectively. Using static digital pathology, images were acquired with a camera attached to a microscope, remotely diagnosed by a pathologist, and then compared to a reference diagnosis.[43] A more recent study by Angelini et al. reported fair interobserver concordance among pathologists (κ = 0.20–0.40) when assessing a set of 20 endomyocardial biopsies (EMBs). The interobserver agreement increased when pathologists were stratified according to their expertise in heart transplant pathology.[44]
Table 3

Summary of papers dealing with posttransplantation heart and lung graft biopsy

Author, yearType of digital pathologyNumber of patients/biopsiesType of biopsyInterventionControls or comparisonsOutcomes/Aim of the studyResults
Armstrong etal., 1998LM plus DIA101EMBsDIA software assessment of fibrosis and myocyte diameter in recipientsDIA software assessment of fibrosis and myocyte diameter in controlsDescriptive differences between the groupsLarger myocyte diameter in transplanted hearts; fibrosis higher in the first posttransplant EMBs
Marchevsky etal., 2002Static LM108Graft lung and heart biopsyTelepathology diagnosisPrevious LM diagnosesAgreement rates, descriptive96% agreement, κ=0.92, for lung biopsies, 82.8% agreement, κ=0.692, for EMBs
Law etal., 2005LM plus DIA25Graft lung biopsyDIA software quantification of basement membrane thicknessNoneCorrelation of basement membrane thickness with the development of bronchiolitis obliterans syndromeStrong negative correlation of basement membrane thickness versus time
Ward etal., 2005LM plus DIA30 (21 biopsies)Graft lung biopsyDIA software assessment of basement membrane thickeningPublished data on basement membrane thickening in other lung diseasesDescriptive results in lung recipients and correlation with respiratory function parametersHigher basement membrane thickening compared to published data in other lung diseases; no correlation with lung function
Sorrentino etal., 2006LM plus DIA21 (361 biopsies)EMBsDIA of IHC stainingNoneDescriptiveRole of IHC assessment in grading rejection
Zakliczynski etal., 2006LM plus DIA43 (129 biopsies)EMBsAutomated software quantification of nucleiNoneDescriptiveRole of chromatin distribution in nuclei to assess severity of rejection
Nozynski etal., 2007LM plus DIA31EMBsUse of ATGStandard treatmentDescriptive differences in quantitative assessment of nuclear parameters with automated software in the groupsNuclear parameters of rejection lower in the ATG group
Angelini etal., 2011WSI20EMB18 pathologists reading WSI slidesIndex diagnosis of referent pathologistInterobserver reproducibility and agreement with referenceFair-to-moderate reproducibility (κ=0.39, α=0.55); role of expertise for agreement with reference diagnosis
Moreira etal., 2011LM plus DIANot stated, 658 imagesEMBsFractal dimension by DIA softwareNoneDescriptive relation between fractal dimension and degrees of rejectionFractal dimension can discriminate between degrees of rejection
Revelo etal., 2012WSI plus DIA22EMBsMicrovessel density in recipients with AMRMicrovessel density in recipients without AMRDescriptiveSignificantly reduced microvessel density in a subset of patients with pathologic AMR with worse outcome
Devitt etal., 2013LM plus DIA34Transplanted hearts in deceased recipientsMeasurement on acquired imagesNoneDescriptiveConsideration of donor-derived accelerated atherosclerosis in heart recipients
Pijet etal., 2014LM plus DIA40EMBsFractal parameters assessment with DIA softwareNoneDescriptive differences between grades of rejectionSome digital parameters can aid grading of rejection
Tona etal., 2014LM plus DIA28EMBsEverolimusMycophenolate mofetilDifference in fibrosis, microvascular remodeling, and arteriolar thickeningCapillary density and fibrosis comparable between groups, arteriolar thickening lower in the everolimus group
Welsh etal., 2016LM plus DIA13EMBsDIA software assessment of IHC stainingNoneEvaluation of Sirt-1 expression in acute cellular rejectionIncreased expression of Sirt-1 in lymphocytes in acute cellular rejection
Feingold etal., 2017WSI plus DIA9EMB with LGDEMBs with WSI9 matched control EMBs with WSIAutomated quantification of fibrosis and microvascular changesGreater fibrosis and microvascular changes in LGD cases
Van den Bosch etal., 2017WSI plus DIA plus confocal microscopy25 (50 EMBs)EMBsEMBs at time of rejectionEMBs at no rejection timeDifference in monocyte and macrophage infiltration and degree of fibrosisCD16+monocyte, M2 macrophage infiltration, and higher fibrosis are associated with rejection

ATG: Anti-thymocyte globulin, DIA: Digital image analysis, EMBs: Endomyocardial biopsies, IHC: Immunohistochemistry, LGD: Late graft dysfunction, LM: Light microscopy, WSI: Whole-slide imaging, AMR: Antibody-mediated rejection

Summary of papers dealing with posttransplantation heart and lung graft biopsy ATG: Anti-thymocyte globulin, DIA: Digital image analysis, EMBs: Endomyocardial biopsies, IHC: Immunohistochemistry, LGD: Late graft dysfunction, LM: Light microscopy, WSI: Whole-slide imaging, AMR: Antibody-mediated rejection Most of the studies (9/16, 56.3%) dealt with graft rejection and quantification of parameters that aid in grading the severity of rejection or help elucidate potential pathogenetic mechanisms. Features quantified with DIA software included myocyte diameter,[45] fibrosis with Masson's trichrome stain,[4546] microvasculature density with CD31[46] or CD34,[47] patterns of inflammatory and immunological cells,[48] monocytes and macrophage profiles,[49] expression of Sirt1, CD8, and FoxP3 on lymphocytes in rejection specimens,[50] and chromatin remodeling expressed as mean gray level.[51] In some publications, digital images were converted in formats adequate for fractal analysis to quantify the inflammatory infiltrate and signs of myocyte damage; it was shown that this kind of DIA can discriminate among different grades of rejection.[5253] Other parameters assessed on graft biopsy with DIA software on LM images (nuclear parameters of cardiomyocytes[54] or fibrosis with Azan-Mallory stain and microvascular remodeling with IHC staining[55]) were relevant for recipient outcome of different immunosuppressive treatments. Overall, the quantitative assessment of EMBs by means of DIA provided more information than routine, semi-quantitative investigation, even if the application of DIA software required a more reproducible staining quality among slides and a better than routine quality of histological slides.[54] Image analysis was also used to quantify macrophages and T-lymphocytes in autopsy specimens of coronary vessels of transplanted heart recipients to compare several vascular remodeling features.[56] Finally, only two studies concerned lung biopsies and both explored the correlation of basement membrane thickness measured with DIA software with the development of bronchiolitis obliterans in recipients. They found that increased thickness of the basement membrane can be transient and not correlated to respiratory function decline.[5758] For the majority of the aforementioned studies, DIA was carried out on static digital images acquired with an LM. Only three out of 14 studies where DIA was employed used WSI technology. This is not surprising given that WSI adoption was only adopted more recently.

Kidney graft biopsy

The studies concerning posttransplant kidney graft biopsies are summarized in Table 4. Articles concerning the posttransplantation kidney biopsy were the most numerous (47/75, 62.7%) and dealt with various topics. Apart from the studies by Minervini et al.[4] and Ito et al.[32] that also included kidney biopsies, nine out of 47 studies (19.1%) addressed agreements between LM and digital slide assessment for several parameters.[596061626364656667] Ito et al. used a static telepathology system and only evaluated the concordance rate,[59] while more recent studies used WSI and achieved good or substantial (κ > 0.40 and κ > 0.60) intra- and inter-observer agreements, concluding that WSI is as reliable as LM for graft biopsy evaluation.[6465] Older studies used LM plus DIA software for the quantification of fibrosis, inflammation, and glomerular sclerosis, reporting that DIA assessment had good correlation with manual evaluation, but that it had higher correlation with graft outcome.[606162] More recent studies combining WSI with DIA for the quantification of C4d IHC,[63] fibrosis with PAS staining and collagen IHC,[66] and CD3 for acute rejection[67] showed that digital evaluation had better correlation with organ function and higher reproducibility than LM assessment.[636667]
Table 4

Summary of papers dealing with posttransplantation kidney graft biopsy

Author, yearType of digital pathologyNumber of patients/biopsiesType of biopsyInterventionControls or comparisonsOutcomes/Aim of the studyResults
Ito etal., 1994Static LM22Graft liver and kidney biopsyTelepathology diagnosisDirect LM diagnosisDescriptive resultsAgreement in 10/12 kidney biopsies and in 9/10 liver biopsies
Gandaliano etal., 1997LM plus DIA20Graft kidney biopsyDIA assessment of IHC staining for CD68 and MCP-1 in acute rejection biopsiesDIA assessment of IHC staining for CD68 and MCP-1 in tubular damage and control biopsiesDescriptive differences in expression between groups and correlation with graft outcomeMCP-1 expression significantly higher in acute rejection biopsies
Grimm etal., 1999LM plus DIA32Graft kidney biopsyDIA assessment of IHC staining of cellular infiltrate in clinical and subclinical rejection biopsiesDIA assessment of IHC staining of cellular infiltrate in normal controlsDescriptive differences in IHC staining among the groupsSignificantly higher infiltration of CD8 and CD68 positive cells in clinical rejection
Nicholson etal., 1999LM plus DIA52Graft kidney biopsySemiautomatic DIA assessment of interstitial fibrosis with IHCNoneDescriptive correlation of interstitial fibrosis with graft outcomePositive correlation of interstitial fibrosis as stained area with eGFR
Bonsib etal., 2000LM plus DIA14 (42 biopsies)Graft kidney biopsyTubular membrane breaks with methenamine silver assessed on digital imagesNoneDescriptive correlation with clinical parametersCorrelation of tubular membrane breaks with creatinine level
Furukuwa etal., 2001LM plus DIA21Graft kidney biopsyDIA software assessment of interstitial fibrosisNoneDescriptive correlation of degree of interstitial fibrosis with graft outcomeUsefulness of the computerized imaging diagnosis for quantitative evaluation of interstitial fibrosis in predicting graft failure
Ishimura etal., 2001LM plus DIA21Graft kidney biopsyDIA software assessment of interstitial fibrosisNoneDescriptive correlation between interstitial fibrosis and TGF=beta IHC stainingStrong association between extracellular TGF beta expression and long-term decline in graft function and increased interstitial fibrosis
Ito etal., 2001Static LM31 (37 biopsies)Graft kidney biopsyTelepathology diagnosisDirect LM diagnosisDescriptive resultsAgreement on diagnosis in 30/37cases
Minervini etal., 2001Static LM102Various case types, among which 9 kidney graft biopsiesConsultant telepathology reviewReferring pathologist original diagnosisAgreement rates, descriptive86% agreement and 14%(only 3% major) disagreement between referring and consultant pathologist
Danilewicz etal., 2003LM plus DIA34Graft kidney biopsyDIA assessment of IHC staining and glomerular area in biopsies with acute rejectionDIA assessment of IHC staining and glomerular area in normal controlsDescriptive differences in IHC staining between the two groupsSignificantly higher cellular infiltrate, glomerular area and interstitial area in acute rejection biopsies
Encarnacion etal., 2003LM plus DIA49Graft kidney biopsyDifferent computerized strategies of DIAExpert pathologist with LMCorrelation of tubulointerstitial fibrosis with graft functionDifferent degree of correlation with graft function of tubulointerstitial fibrosis scored with different strategies
Grimm etal., 2003LM plus DIANAGraft kidney biopsyAutomated DIA software assessment of interstitial fibrosisNoneCorrelation of interstitial fibrosis with graft outcomeCortical fractional interstitial fibrosis volume can be a surrogate for time to graft failure
Mui etal., 2003LM plus DIA30Graft kidney biopsyDIA assessment of IHC staining in ischemic injuryDIA assessment of IHC staining in normal controlsDescriptiveDifferent pattern of expression of markers in ischemic injury biopsies
Pape etal., 2003LM plus DIA56Graft kidney biopsyDIA assessment of interstitial fibrosisNoneCorrelation of interstitial fibrosis with graft outcomeQuantitative measurement of fibrosis by picrosirius red staining is a prognostic indicator for estimating long-term graft function
Sugiyama etal., 2003LM plus DIA25Graft kidney biopsyDIA assessment of mean glomerular area and interstitial areaNoneDescriptive differences in recipients with or without focal segmental glomerulosclerosisNo significant difference in mean glomerular area nor interstitial area between the two groups
Bains etal., 2004LM plus DIA112Graft kidney biopsyDIA software assessment of fibrosis in DCD and DBD graft biopsiesNoneDifference of fibrosis in the two groupsNo significant differences in level of fibrosis
Danilewicz etal., 2004LM plus DIA35Graft kidney biopsyDIA quantification of mast cells and leukocytes with IHC staining in acute rejection biopsiesDIA quantification of mast cells and leukocytes with IHC staining in normal controlsDescriptive differences between the groupsSignificantly higher number of mast cells and leukocytes in acute rejection; positive correlation between inflammatory infiltrate and interstitial area
Pape etal., 2004LM plus DIA56Graft kidney biopsyRenal resistance index with DopplerInterstitial fibrosis assessment with DIACorrelation between the two measurements and with graft outcomePositive correlation between the two measures and of the combination of the two with graft outcome
Sarioglu etal., 2004LM plus DIA15Graft kidney biopsyAutomated quantification of stained areaNoneDescriptiveStrong correlation between stained area and serum creatinine(r=0.64)
Sund etal., 2004LM plus DIA33Graft kidney biopsyDIA automated quantificationPathologist with LMDescriptiveSignificant correlation between the two modalities and with graft outcome
Nishi etal., 2005LM plus DIA14Graft kidney biopsyDIA software assessment of the peritubular capillary network in recipients with rejectionDIA software assessment of the peritubular capillary network in recipients without rejectionDescriptiveSignificant differences in surface areas of tubulin and glomerular diameter between the groups
Sis etal., 2005LM plus DIA57 (75 biopsies)Graft kidney biopsyDIA software assessment of stained areaNoneDescriptive correlation among stained areas for fibrosis, Banff scores and rejectionNo significant association between serum creatinine at time of biopsy and percentage of stained areas for fibrosis; no predictive value for rejection
Danilewicz etal., 2006LM plus DIA33Graft kidney biopsyDIA of IHC staining in acute rejection recipientsDIA of IHC staining in recipients with no rejectionDifferences in IHC staining in the two groupsHigher expression of TGF beta, CD3, CD8 in acute rejection
Hoffman etal., 2006LM plus DIA138Graft kidney biopsyDIA of IHC stainingNoneDescriptive expression of CXCR3Higher expression of CXCR3 in acute rejection
Lauronen etal., 2006LM plus DIA35Graft kidney biopsyDIA software scoringPathologist with LMDescriptiveNo significant difference in scoring between the modalities
Roos-van- Groningen etal., 2006LM plus DIA54 (108 biopsies)Graft kidney biopsyCyclosporineTacrolimusFibrosis and IHC staining assessed by automated DIA softwareNo quantitative differences in fibrosis and IHC staining between cyclosporine and tacrolimus
Rowshani etal., 2006LM plus DIA126Graft kidney biopsyCyclosporineTacrolimusFibrosis with Sirius red assessed by automated DIA softwareNo difference in the degree of interstitial stained area between the two treatment groups
Sarioglu etal., 2006LM plus DIA37 (44 biopsies)Graft kidney biopsyDIA assessment of periodic acid methenamine silver stainingNoneDescriptive relation of stained area to Banff scores and creatinine valuesStrong association of stained area with increased interstitial fibrosis and tubular atrophy Banff scores
Scholten etal., 2006LM plus DIA126Graft kidney biopsyCyclosporineTacrolimusSubacute rejection assessed by pathologist and automated fibrosis quantificationNo quantitative differences in fibrosis between cyclosporine and tacrolimus; higher prevalence of subacute rejection in the cyclosporine group but no difference in graft survival
Servais etal., 2007LM plus DIA26Graft kidney biopsyDIA automated quantification of interstitial fibrosis in recipients treated with cyclosporineNoneDescriptive correlation of interstitial fibrosis with graft outcomeCorrelation of higher grade of automated interstitial fibrosis with a higher creatinine
Servais etal., 2007LM plus DIA26Graft kidney biopsyDIA automated quantification of interstitial fibrosis in recipients treated with cyclosporineNoneDescriptive correlation of interstitial fibrosis with graft outcomeAssociation between high grade of automated interstitial fibrosis and worsening of creatinine clearance
Birk etal., 2010LM plus DIA29 (105 biopsies)Graft kidney biopsyDIA software quantification of interstitial fibrosisNoneDescriptive correlation of interstitial fibrosis and graft outcomeSignificant correlation of interstitial fibrosis assessed by DIA software with graft outcome
Yan etal., 2010LM plus DIA46Graft kidney biopsyDIA quantification of IHC stainingNoneCorrelation of IHC staining with Banff score for interstitial fibrosis and tubular atrophyHigher IHC staining expression in higher Banff score classes for interstitial fibrosis and tubular atrophy
Brazdziute etal., 2011WSI plus DIA32 (34 biopsies)Graft kidney biopsyAutomated software on WSIPathologist on LMCorrelation and interobserver variability in C4d scoringGood-to-high correlation between pathologist and automated software; good manual-automated interobserver agreement
Meas-Yedid etal., 2011WSI plus DIA90 biopsiesGraft kidney biopsyAutomated software on WSIExpert pathologist on LMCorrelation and interobserver variability in interstitial fibrosis scoringGood agreement between the two methods(κ=0.75)
Miura etal., 2011LM plus DIA109Graft kidney biopsyDIA software assessment of interstitial fibrosisNoneCorrelation of interstitial fibrosis different tacrolimus regimens and cytochrome polymorphismHigher increase in interstitial fibrosis in absence of cytochrome polymorphism
Servais etal., 2011LM plus DIA140Graft kidney biopsyAutomated DIA software assessment of interstitial fibrosisNoneCorrelation of interstitial fibrosis with graft outcomeCorrelation between interstitial fibrosis at different time points and eGFR
Becker etal., 2012LM plus DIA40Graft kidney biopsyIHC staining in cellular infiltrate of clinical, operational tolerance recipientsIHC staining in cellular infiltrate of rejection recipientsDescriptive expression of IHC staining in inflammatory infiltrateDifferent IHC staining in the two groups
Ozluk etal., 2012WSI40Graft kidney biopsyPathologists with WSIPathologists with LMIntra- and inter-observer reproducibilityComparable intraobserver reproducibility for both modalities; higher interobserver reproducibility with WSI
Yan etal., 2012LM plus DIA28Graft kidney biopsyDIA software quantification of IHC staining of GSK3 beta at different levels of inflammationNoneDescriptive correlation between GSK3 beta staining and inflammationStronger GSK3 beta expression with increasing grade of inflammation or interstitial fibrosis/tubular atrophy
Yan etal., 2012LM plus DIA61Graft kidney biopsyDIA software quantification of IHC staining in recipients with AMRDIA software quantification of IHC staining in recipients without AMRDescriptive relationship of IHC staining of extracellular matrix cytokines with interstitial fibrosis and creatinineHigher expression in grafts with AMR; increasing expression with higher Banff scores of interstitial fibrosis and positive correlation with creatinine
Caplin etal., 2013LM plus DIA246Graft kidney biopsySerial posttransplant biopsiesNo serial biopsiesDescriptive correlation of index of chronic damage with graft functionNo significant differences between the two groups; index of chronic damage not predictive of graft function
Jen etal., 2013WSI25Graft kidney biopsyExpert pathologists with WSIExpert pathologist with LMIntra- and inter-observer concordanceSubstantial intraobserver concordance between modalities (κ=0.60), moderate interobserver concordance (κ=0.41-0.45)
Farris etal., 2014WSI plus DIA30Graft kidney biopsiesPathologists scoring interstitial fibrosis on WSI slides with different stainsComputerized DIA of collagen IHC stainingInterobserver reproducibility and correlation of visual assessment on WSI with DIA assessment and with graft outcomePoor reproducibility between pathologists; moderate correlation of visual assessment with DIA assessment of collagen-IHC; moderate correlation with graft outcome with no significant differences between the modalities
Vuiblet etal., 2015LM plus DIA plus spectroscopy (FTIR)106 (166 biopsies)Graft kidney biopsySpectroscopyPathologist with LM and DIAQuantification of interstitial fibrosis and inflammationPoor agreement between scoring LM versus DIA and LM versus FTIR, good agreement in percentages between DIA and FTIR; good correlation between fibrosis with FTIR and graft function
Hara etal., 2016LM plus DIA934Graft and native kidney biopsy426 graft biopsy508 native kidney biopsyQuantification of GSECsPrevalence of GSECs slightly increased with posttransplant duration but not statistically significant
Yan etal., 2016LM plus DIA50Graft kidney biopsyDIA software assessment of IHC staining in graft with chronic dysfunctionDIA software assessment of IHC staining in graft with no dysfunctionDifference in markers expression and correlation with Banff scores for interstitial fibrosis/tubular atrophyHigher expression in grafts with dysfunction; positive correlation between marker expression and Banff scores
Bräsens etal., 2017WSI plus DIA67Graft kidney biopsyAutomated software on WSINoneCorrelation of different cellular types digitally quantified with graft functionPredictive value of digitally quantified CD68 cell density for graft function
Moon etal., 2017WSI plus DIA45Graft kidney biopsyDIA automated software assessment of interstitial inflammation with different algorithmsVisual assessment of interstitial inflammationDescriptive correlation among the modalitiesQuantitation algorithms correlated between each other and also with visual assessment

AMR: Antibody-mediated rejection, DBD: Donor after brain death, DCD: Donor after cardiac death, DIA: Digital image analysis, eGFR: Estimated glomerular filtration rate, FTIR: Fourier-transformed infrared spectroscopy, GSECs: Granular swollen epithelial cells, IHC: Immunohistochemistry, LM: Light microscopy, WSI: Whole-slide imaging, MCP-1: Monocyte chemotactic peptide-1, TGF: Transforming growth factor

Summary of papers dealing with posttransplantation kidney graft biopsy AMR: Antibody-mediated rejection, DBD: Donor after brain death, DCD: Donor after cardiac death, DIA: Digital image analysis, eGFR: Estimated glomerular filtration rate, FTIR: Fourier-transformed infrared spectroscopy, GSECs: Granular swollen epithelial cells, IHC: Immunohistochemistry, LM: Light microscopy, WSI: Whole-slide imaging, MCP-1: Monocyte chemotactic peptide-1, TGF: Transforming growth factor Most of the studies on graft kidney biopsy use DIA techniques to explore the role of several biopsy features ranging from fibrosis evaluated with special stains to the expression and quantification of specific IHC markers in determining organ outcome,[68697071727374757677787980818283] as well as signs of acute rejection.[84858687888990919293] In all of these studies, there is no direct comparison of DIA evaluation with manual pathologist results. Moreover, most of these are retrospective or case–control observational studies. The most studied parameter was interstitial fibrosis, with the correlation of DIA quantitative assessment to organ outcome being the main focus of these studies. Interstitial fibrosis was highlighted with special stains or with IHC, and some studies included comparison with other techniques such as spectroscopy[74] or Doppler ultrasound for renal resistance index.[81] Even though organ outcome was assessed slightly differently, most of these studies reinforced the idea that precise and automated quantification of this parameter by DIA technique can add value to biopsy evaluation, providing more reproducible results and permitting comparisons to be made with findings from other researchers. Similarly, studies about rejection mostly compared the IHC expression of several inflammatory markers and immune system cellular infiltration evaluated with DIA software in rejection biopsies and normal control biopsies. The remaining studies on posttransplantation kidney biopsy explored other features that correlated with ischemic injury,[94] levels of glomerular sclerosis,[95] fibrosis in grafts from after-brain-death donor or cardiac-death donor,[96] IHC markers to quantify interstitial fibrosis,[979899] correlation with Banff score parameters[100] and more subtle features such as swollen glomerular epithelial cells.[101] Finally, three studies from the same research group compared fibrosis, assessed with special stains or IHC, and quantified by DIA software, in patients receiving cyclosporine or tacrolimus.[102103104]

Two main research themes: concordance and correlation to outcome

As already mentioned, the main issues addressed overall were the concordance between standard LM or manual assessment and WSI or DIA instruments and the correlation of histological features assessed by DIA methods with the outcome. The first topic was the most frequent in pretransplant papers. Intra- and inter-observer concordance with κ index was high when comparing WSI with LM,[1526] thus reinforcing the point that digital diagnosis could replace conventional glass-slide diagnosis. The group of studies concerning pancreatic islet counting,[2728293031] even with slightly different statistical measures, however, pointed toward the same direction, stating that DIA assessment is highly correlated to manual standard assessment and had the advantage of lesser interoperator variability. This remained true also in posttransplant papers addressing the same topic, even if less numerous.[3334446364656667] In particular, more recent studies combining DIA with WSI concluded that DIA assessment of features has not only higher reproducibility than LM but also a better correlation to graft outcome, thus embracing with the second more frequent topic encountered through papers. This applies particularly to liver and kidney graft pathology, where a quota of papers compared DIA to manual assessment of features on LM-digitized images and correlated to outcome. With different grade of strength, they all suggested a better correlation to outcome and the advantage of a higher reproducibility. However, the vast majority of these studies were retrospective, both in the case of only concordance/reproducibility studies and of correlation-to-outcome studies, with the use of archival cases where the reference diagnosis was made previously with LM and sometimes with partly overlapping case populations.[35363738] Even if a quality assessment of studies was beyond the aims of this work, it is noticeable that only few studies were multicentric with the involvement of pathologists not working together, thus minimizing possible bias.[334466] Moreover, in the majority of studies, digital pathology pertained only to the research field, especially in case of assessment of histological features or particular IHC marker expression, but also for concordance studies, where the value of digital pathology is explored in view of a possible future clinical full implementation.

CONCLUSION AND FUTURE DIRECTIONS

The aim of this review was to provide a broad overview of accrued international experience in the use of digital pathology in transplantation. Most retrieved studies involved the evaluation of the posttransplantation biopsy. The acquisition, manipulation, and eventual transmission of digital slides, before the advent of WSI, were too slow to be compatible with the time-sensitive needs encountered in the preimplantation setting. DIA was more adequate for outcome studies where time is not necessarily an issue. It is not surprising that most of the studies using WSI, in particular, those in the pretransplant context, focused on the diagnostic agreement and concordance between LM and WSI. Indeed, it is likely that WSI may soon replace conventional LM diagnosis, especially as newer generation scanners acquire higher resolution images and digital platforms facilitate easier sharing of digital slides among pathologists. Some conventional barriers to implementation of WSI such as costs and storage issues could now be overcome in big centers and academic institutions. Some questions remain open, mainly concerning the regulatory constraints in different countries and economic issues on payer/reimbursement that apply particularly to the transplantation setting, for example, for second-opinion consultations and quality control programs, as transplantation activity is traditionally managed by public national health system. The number of studies about WSI coupled with DIA is relatively small and restricted to the last 8 years. However, it is foreseeable that in the future, there will be a growing number of studies applying DIA and most likely deep learning algorithms and artificial intelligence to WSI, thereby augmenting the practice and field of transplantation.[8]

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest
  104 in total

1.  Computerized histomorphometric assessment of protocol renal transplant biopsy specimens for surrogate markers of chronic rejection.

Authors:  M L Nicholson; E Bailey; S Williams; K P Harris; P N Furness
Journal:  Transplantation       Date:  1999-07-27       Impact factor: 4.939

2.  Prediction of chronic allograft failure using computerized image analysis of postperfusion biopsy specimen: study of cadaver kidney transplants.

Authors:  T Furukawa; T Kinukawa; S Sugiyama; Y Ono; S Ohshima
Journal:  Transplant Proc       Date:  2001 Feb-Mar       Impact factor: 1.066

3.  Clinical rejection is distinguished from subclinical rejection by increased infiltration by a population of activated macrophages.

Authors:  P C Grimm; R McKenna; P Nickerson; M E Russell; J Gough; E Gospodarek; B Liu; J Jeffery; D N Rush
Journal:  J Am Soc Nephrol       Date:  1999-07       Impact factor: 10.121

4.  Acute rejection-associated tubular basement membrane defects and chronic allograft nephropathy.

Authors:  S M Bonsib; S R Abul-Ezz; I Ahmad; S M Young; E N Ellis; D L Schneider; P D Walker
Journal:  Kidney Int       Date:  2000-11       Impact factor: 10.612

5.  Development and experience with an integrated system for transplantation telepathology.

Authors:  M I Minervini; Y Yagi; I R Marino; A Lawson; M Nalesnik; P Randhawa; T Wu; J J Fung; A Demetris
Journal:  Hum Pathol       Date:  2001-12       Impact factor: 3.466

6.  Telepathology for the biopsy specimens from human allografted kidney: effectiveness and pitfalls.

Authors:  H Ito; K Shomori; H Adachi; K Taniyama
Journal:  Clin Transplant       Date:  2001       Impact factor: 2.863

7.  Effect of ischemia-reperfusion on bile canalicular F-actin microfilaments in hepatocytes of human liver allograft: image analysis by confocal laser scanning microscopy.

Authors:  L Benkoël; F Dodero; J Hardwigsen; P Campan; D Botta-Fridlund; D Lombardo; Y P Le Treut; A Chamlian
Journal:  Dig Dis Sci       Date:  2001-08       Impact factor: 3.199

8.  Internet teleconferencing method for telepathology consultations from lung and heart transplant patients.

Authors:  Alberto M Marchevsky; Sean K Lau; Elham Khanafshar; Christopher Lockhart; Ann Phan; Paul J Michaels; Michael C Fishbein
Journal:  Hum Pathol       Date:  2002-04       Impact factor: 3.466

9.  Clinical relevance of immunohistochemical staining for ecto-AMPase and ecto-ATPase in chronic allograft nephropathy (CAN).

Authors:  Kwok W Mui; Willem J van Son; Anton T M G Tiebosch; Harry van Goor; Winston W Bakker
Journal:  Nephrol Dial Transplant       Date:  2003-01       Impact factor: 5.992

10.  Transforming growth factor-beta1 expression in early biopsy specimen predicts long-term graft function following pediatric renal transplantation.

Authors:  T Ishimura; M Fujisawa; S Isotani; A Higuchi; K Iijima; S Arakawa; K Hohenfellner; K C Flanders; N Yoshikawa; S Kamidono
Journal:  Clin Transplant       Date:  2001-06       Impact factor: 2.863

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  11 in total

1.  Resolution-based distillation for efficient histology image classification.

Authors:  Joseph DiPalma; Arief A Suriawinata; Laura J Tafe; Lorenzo Torresani; Saeed Hassanpour
Journal:  Artif Intell Med       Date:  2021-08-06       Impact factor: 7.011

2.  Deceased Donor Procurement Biopsy Practices, Interpretation, and Histology-Based Decision-Making: A Survey of US Kidney Transplant Centers.

Authors:  Krista L Lentine; Vidya A Fleetwood; Yasar Caliskan; Henry Randall; Jason R Wellen; Melissa Lichtenberger; Craig Dedert; Richard Rothweiler; Gary Marklin; Diane Brockmeier; Mark A Schnitzler; Syed A Husain; Sumit Mohan; Bertram L Kasiske; Matthew Cooper; Roslyn B Mannon; David A Axelrod
Journal:  Kidney Int Rep       Date:  2022-03-28

3.  Variations in deceased donor kidney procurement biopsy practice patterns: A survey of U.S. organ procurement organizations.

Authors:  Brendan R Emmons; S Ali Husain; Kristen L King; Joel T Adler; Sumit Mohan
Journal:  Clin Transplant       Date:  2021-07-14       Impact factor: 3.456

4.  Commentary: The Digital Fate of Glomeruli in Renal Biopsy.

Authors:  Ilaria Girolami; Stefano Marletta; Albino Eccher
Journal:  J Pathol Inform       Date:  2021-03-22

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

Authors:  Flavia Neri; Albino Eccher; Paolo Rigotti; Ilaria Girolami; Gianluigi Zaza; Giovanni Gambaro; MariaGaia Mastrosimini; Giulia Bencini; Caterina Di Bella; Claudia Mescoli; Luigino Boschiero; Stefano Marletta; Paolo Angelo Dei Tos; Lucrezia Furian
Journal:  J Pathol Inform       Date:  2021-11-01

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

Authors:  Stefano Marletta; Liron Pantanowitz; Deborah Malvi; Luca Novelli; Claudia Mescoli; Massimo Cardillo; Antonietta D'Errico; Ilaria Girolami; Albino Eccher
Journal:  Acad Pathol       Date:  2022-07-31

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

Authors:  Ilaria Girolami; Stefania Neri; Albino Eccher; Matteo Brunelli; Mattew Hanna; Liron Pantanowitz; Esther Hanspeter; Guido Mazzoleni
Journal:  Digit Health       Date:  2022-07-29

8.  The Independent Effects of Procurement Biopsy Findings on 10-Year Outcomes of Extended Criteria Donor Kidney Transplants.

Authors:  Darren E Stewart; Julia Foutz; Layla Kamal; Samantha Weiss; Harrison S McGehee; Matthew Cooper; Gaurav Gupta
Journal:  Kidney Int Rep       Date:  2022-05-30

Review 9.  Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review.

Authors:  Ilaria Girolami; Liron Pantanowitz; Stefano Marletta; Meyke Hermsen; Jeroen van der Laak; Enrico Munari; Lucrezia Furian; Fabio Vistoli; Gianluigi Zaza; Massimo Cardillo; Loreto Gesualdo; Giovanni Gambaro; Albino Eccher
Journal:  J Nephrol       Date:  2022-04-19       Impact factor: 4.393

Review 10.  Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders.

Authors:  Rossana C N Melo; Maximilian W D Raas; Cinthia Palazzi; Vitor H Neves; Kássia K Malta; Thiago P Silva
Journal:  Front Med (Lausanne)       Date:  2020-01-08
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