An automated histological classification system for precision diagnostics of kidney allografts

Donor-derived Cell-free DNA as a Noninvasive Biomarker of Kidney Allograft Rejection in Pediatric Kidney Transplantation

BACKGROUND

Allograft biopsy remains the gold standard to diagnose rejection. New noninvasive biomarkers are needed to avoid unnecessary biopsies and to diagnose early rejection. We studied the performance of donor derived cell-free DNA (dd-cfDNA) to detect rejection in an unselected cohort of pediatric kidney transplant recipients (pKTRs) and determined whether dd-cfDNA could improve standard-of-care monitoring and detection of kidney allograft rejection in children.

METHODS

We included 196 pKTRs, who underwent 367 biopsies with concomitant dd-cfDNA assessment. We assessed the association of dd-cfDNA with histological lesions and with rejection using a Cox regression model and compared the discrimination of 3 models: standard of care, dd-cfDNA alone, and combined.

RESULTS

We found a significant increase in dd-cfDNA levels with higher degree of inflammation on the biopsies. dd-cfDNA was strongly and independently associated with the presence of allograft rejection (odds ratio 1.89, 95% confidence interval [CI], 1.40-2.60). dd-cfDNA alone had a fair discrimination of 0.76 (95% CI, 0.69-0.81) to detect rejection and its addition to standard of care resulted in a significant increase in discrimination from 0.80 (95% CI, 0.71-0.85) to 0.84 (95% CI, 0.79-0.90), P = 0.01.

CONCLUSIONS

dd-cfDNA combined with standard of care improves the prediction of rejection in pKTRs. Further specific studies are needed to better define how to use this promising biomarker in various contexts of use in children.

Theranostic methodology for ex vivo donor lung rehabilitation

BACKGROUND

About 80% of donor lungs are not utilized for transplantation. Cross-circulation of ex vivo lungs with a support swine enables the rehabilitation of donor lungs that are initially deemed unsuitable for transplantation. Robust therapeutic and diagnostic modalities are needed for ex vivo lung rehabilitation; however, no standardized "theranostic" methodology has been reported.

METHODS

Ex vivo lungs (n = 23; 17 injured and 6 controls) with multi-focal contusion (n = 6, human), gastric aspiration injury (n = 8, swine), ischemia-reperfusion injury (n = 3, swine), or no injury (n = 6, swine) were used to develop a therapeutic and diagnostic (theranostic) methodology for ex vivo lung rehabilitation during cross-circulation. Airway (bronchoscopic, nebulized), intravascular, and transpleural access enabled sample collection and therapeutic delivery. Diagnostic modalities included non-invasive imaging, functional testing, and molecular assays. Therapeutic modalities included bronchoalveolar lavage, surfactant replacement, recruitment maneuvers, and cell/organoid delivery. Real-time tracking of delivered cells was performed via fluorescence and bioluminescence imaging.

FINDINGS

Diagnostic assessments revealed tissue-, cell-, and molecular-level insights at global and regional scales of ex vivo lungs during cross-circulation, which informed therapeutic management and interventions to recover donor lungs. Mesenchymal stromal cells and lung organoids were delivered bronchoscopically and transpleurally, tracked non-invasively during cross-circulation, and observed to localize within the parenchyma.

CONCLUSIONS

Application of a theranostic methodology during cross-circulation enabled real-time ex vivo lung assessment and rehabilitation across a variety of lung injuries to help increase clinical utilization of donor lungs in the future.

FUNDING

NIH (P41 EB027062, R01HL120046, U01HL134760), CFF (VUNJAK23XX0).

Microvascular Inflammation of Kidney Allografts and Clinical Outcomes

BACKGROUND

The heterogeneous clinical presentation of graft microvascular inflammation poses a major challenge to successful kidney transplantation. The effect of microvascular inflammation on allograft outcomes is unclear.

METHODS

We conducted a cohort study that included kidney-transplant recipients from more than 30 transplantation centers in Europe and North America who had undergone allograft biopsy between 2004 and 2023. We integrated clinical and pathological data to classify biopsy specimens according to the 2022 Banff Classification of Renal Allograft Pathology, which includes two new diagnostic categories: probable antibody-mediated rejection and microvascular inflammation without evidence of an antibody-mediated response. We then assessed the association between the newly recognized microvascular inflammation phenotypes and allograft survival and disease progression.

RESULTS

A total of 16,293 kidney-transplant biopsy specimens from 6798 patients were assessed. We identified the newly recognized microvascular inflammation phenotypes in 788 specimens, of which 641 were previously categorized as specimens with no evidence of rejection. As compared with patients without rejection, the hazard ratio for graft loss was 2.1 (95% confidence interval [CI], 1.5 to 3.1) among patients with microvascular inflammation without evidence of an antibody-mediated response and 2.7 (95% CI, 2.2 to 3.3) among patients with antibody-mediated rejection. Patients with a diagnosis of probable antibody-mediated rejection had a higher risk of graft failure beyond year 5 after biopsy than those without rejection (hazard ratio, 1.7; 95% CI, 0.8 to 3.5). Patients with a diagnosis of either newly recognized microvascular inflammation phenotype had a higher risk of progression of transplant glomerulopathy during follow-up than patients without microvascular inflammation.

CONCLUSIONS

Microvascular inflammation in kidney allografts includes distinct phenotypes, with various disease progression and allograft outcomes. Our findings support the clinical use of additional rejection phenotypes to standardize diagnostics for kidney allografts. (Funded by OrganX. ClinicalTrials.gov number, NCT06496269.).

Banff 2022 Kidney Commentary: Reflections and Future Directions

In September 2022, in Banff, Alberta, Canada, the XVIth Banff meeting, corresponding to the 30th anniversary of the Banff classification, was held, leading to 2 recent publications. Discussions at the Banff meeting focused on proposing improvements to the Banff process as a whole. In line with this, a unique opportunity was offered to a selected group of 16 representatives from the pathology and transplant nephrology community, experts in the field of kidney transplantation, to review these 2 Banff manuscripts. The aim was to provide an insightful commentary, to gauge any prospective influence the proposed changes may have, and to identify any potential areas for future enhancement within the Banff classification. The group expressed its satisfaction with the incorporation of 2 new entities, namely "microvascular inflammation/injury donor-specific antibodies-negative and C4d negative" and "probable antibody-mediated rejection," into category 2. These changes expand the classification, facilitating the capture of more biopsies and providing an opportunity to explore the clinical implications of these lesions further. However, we found that the Banff classification remains complex, potentially hindering its widespread utilization, even if a degree of complexity may be unavoidable given the intricate pathophysiology of kidney allograft pathology. Addressing the histomorphologic diagnosis of chronic active T cell-mediated rejection (CA TCMR), potentially reconsidering a diagnostic-agnostic approach, as for category 2, to inflammation in interstitial fibrosis and tubular atrophy and chronic active T cell-mediated rejection was also an important objective. Furthermore, we felt a need for more evidence before molecular diagnostics could be routinely integrated and emphasized the need for clinical and histologic context determination and the substantiation of its clinical impact through rigorous clinical trials. Finally, our discussions stressed the ongoing necessity for multidisciplinary decision-making regarding patient care.

Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation

Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learning (ML) to answer diagnostic, prognostic, and therapeutic questions for many years. Nevertheless, despite the question of whether AI models add value to traditional modeling approaches, such as regression models, their "black box" nature is one of the factors that have hindered the translation from research to clinical practice. Several techniques that make such models understandable to humans were developed with the promise of increasing transparency in the support of medical decision-making. These techniques should help AI to close the gap between theory and practice by yielding trust in the model by doctors and patients, allowing model auditing, and facilitating compliance with emergent AI regulations. But is this also happening in the field of kidney transplantation? This review reports the use and explanation of "black box" models to diagnose and predict kidney allograft rejection, delayed graft function, graft failure, and other related outcomes after kidney transplantation. In particular, we emphasize the discussion on the need (or not) to explain ML models for biological discovery and clinical implementation in kidney transplantation. We also discuss promising future research paths for these computational tools.

Unveiling Spatial Immune Cell Profile in Kidney Allograft Rejections Using 36-plex Immunofluorescence Imaging

BACKGROUND

Kidney allograft rejections are orchestrated by a variety of immune cells. Because of the complex histopathologic features, accurate pathological diagnosis poses challenges even for expert pathologists. The objective of this study was to unveil novel spatial indices associated with transplant rejection by using a spatial bioinformatic approach using 36-plex immunofluorescence image data.

METHODS

The image obtained from 11 T cell-mediated rejection (TCMR) and 12 antibody-mediated rejection (AMR) samples were segmented into 753 737 single cells using DeepCell's Mesmer algorithm. These cells were categorized into 13 distinct cell types through unsupervised clustering based on their biomarker expression profiles. Cell neighborhood analysis allowed us to stratify kidney tissue into 8 distinct neighborhood components consisting of unique cell type enrichment profiles.

RESULTS

In contrast to TCMR samples, AMR samples exhibited a higher frequency of neighborhood components that were characterized by an enrichment of CD31 + endothelial cells. Although the overall frequency of CD68 + macrophages in AMR samples was not significantly high, CD68 + macrophages within endothelial cell-rich lesions exhibited a significantly higher frequency in AMR samples than TCMR samples. Furthermore, the frequency of interactions between CD31 + cells and CD68 + cells was significantly increased in AMR samples, implying the pivotal role of macrophages in AMR pathogenesis. Importantly, patients demonstrating a high frequency of CD31:CD68 interactions experienced significantly poorer outcomes in terms of chronic AMR progression.

CONCLUSIONS

Collectively, these data indicate the potential of spatial bioinformatic as a valuable tool for aiding in pathological diagnosis and for uncovering new insights into the mechanisms underlying transplant rejection.

The global evolution and impact of systems biology and artificial intelligence in stem cell research and therapeutics development: a scoping review

Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8 to 10-fold increase in research output related to all 3 search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the (US, n = 1487), (UK, n = 1094), Germany (n = 355), The Netherlands (n = 339), Russia (n = 215), and France (n = 149), while for AI-related research the US (n = 853) and UK (n = 258) take a strong lead, followed by Switzerland (n = 69), The Netherlands (n = 37), and Germany (n = 19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection among AI, SysBio, and SC research over the past 2 decades, with substantial growth in all 3 fields and exponential increases in AI-related research in the past decade.

Application of spatial-omics to the classification of kidney biopsy samples in transplantation

Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.

Evaluation of non-invasive biomarkers of kidney allograft rejection in a prospective multicenter unselected cohort study (EU-TRAIN)

Non-invasive biomarkers are promising tools for improving kidney allograft rejection monitoring, but their clinical adoption requires more evidence in specifically designed studies. To address this unmet need, we designed the EU-TRAIN study, a large prospective multicentric unselected cohort funded by the European Commission. Here, we included consecutive adult patients who received a kidney allograft in nine European transplant centers between November 2018 and June 2020. We prospectively assessed gene expression levels of 19 blood messenger RNAs, four antibodies targeting non-human leukocyte antigen (HLA) endothelial antigens, together with circulating anti-HLA donor-specific antibodies (DSA). The primary outcome was allograft rejection (antibody-mediated, T cell-mediated, or mixed) in the first year post-transplantation. Overall, 412 patients were included, with 812 biopsies paired with a blood sample. CD4 gene expression was significantly associated with rejection, while circulating anti-HLA DSA had a significant association with allograft rejection and a strong association with antibody-mediated rejection. All other tested biomarkers, including AKR1C3, CD3E, CD40, CD8A, CD9, CTLA4, ENTPD1, FOXP3, GZMB, ID3, IL7R, MS4A1, MZB1, POU2AF1, POU2F1, TCL1A, TLR4, and TRIB1, as well as antibodies against angiotensin II type 1 receptor, endothelin 1 type A receptor, C3a and C5a receptors, did not show significant associations with allograft rejection. The blood messenger RNAs and non-HLA antibodies did not show an additional value beyond standard of care monitoring parameters and circulating anti-HLA DSA to predict allograft rejection in the first year post-transplantation. Thus, our results open avenues for specifically designed studies to demonstrate the clinical relevance and implementation of other candidate non-invasive biomarkers in kidney transplantation practice.

Systematic Biopsy-Based Transcriptomics and Diagnosis of Antibody-Mediated Kidney Transplant Rejection in Clinical Practice

Key Points

Impact of biopsy-based transcriptomics in clinical practice is still unclear. Biopsy-based transcriptomics is indicated in a significant proportion of kidney transplant biopsies for the diagnosis of antibody-mediated rejection. Biopsy-based transcriptomics is useful for antibody-mediated rejection diagnosis in clinical practice.

Background

To diagnose kidney transplant antibody-mediated rejection (AMR), biopsy-based transcriptomics can substitute for some histological criteria according to the Banff classification. However, clinical accessibility of these assays is still limited. Here, we aimed to evaluate the impact of integrating a routine-compatible molecular assay for the diagnosis of AMR in clinical practice.

Methods

All biopsies performed in our center between 2013 and 2017 were retrospectively included. These biopsies were classified into three groups: AMR biopsies which displayed the full Banff criteria of AMR independently of biopsy-based transcriptomics; undetermined for AMR biopsies which did not meet AMR histological criteria, but would have been considered as AMR if biopsy-based transcriptomics had been positive; and control biopsies which showed no features of rejection.

Results

Within the inclusion period, 342 biopsies had a complete Banff scoring. Thirty-six of the biopsies already met AMR criteria, and 43 of 306 (14%) were considered as undetermined for AMR. Among these biopsies, 24 of 43 (56%) had a molecular signature of AMR, reclassifying them into the AMR category. Five-year death-censored survival of these biopsies was unfavorable and statistically equivalent to that of the AMR category (P = 0.22), with 15 of 24 (63%) graft loss.

Conclusions

A significant proportion of biopsies could benefit from a biopsy-based transcriptomics for AMR diagnosis according to the Banff classification. Using a routine-compatible molecular tool, more than the half of these biopsies were reclassified as AMR and associated with poor allograft survival.

Proceedings of the 2024 Transplant AI Symposium

With recent advancements in deep learning (DL) techniques, the use of artificial intelligence (AI) has become increasingly prevalent in all fields. Currently valued at 9.01 billion USD, it is a rapidly growing market, projected to increase by 40% per annum. There has been great interest in how AI could transform the practice of medicine, with the potential to improve all healthcare spheres from workflow management, accessibility, and cost efficiency to enhanced diagnostics with improved prognostic accuracy, allowing the practice of precision medicine. The applicability of AI is particularly promising for transplant medicine, in which it can help navigate the complex interplay of a myriad of variables and improve patient care. However, caution must be exercised when developing DL models, ensuring they are trained with large, reliable, and diverse datasets to minimize bias and increase generalizability. There must be transparency in the methodology and extensive validation of the model, including randomized controlled trials to demonstrate performance and cultivate trust among physicians and patients. Furthermore, there is a need to regulate this rapidly evolving field, with updated policies for the governance of AI-based technologies. Taking this in consideration, we summarize the latest transplant AI developments from the Ajmera Transplant Center's inaugural symposium.

[Artificial intelligence in kidney transplant pathology]

BACKGROUND

Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology.

AIM

Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide a future outlook.

MATERIALS AND METHODS

Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney". Based on these results and studies cited in the identified literature, a selection was made of studies that have a histopathological focus and use AI to improve kidney transplant diagnostics.

RESULTS AND CONCLUSION

Many studies have already made important contributions, particularly to the automation of the quantification of some histopathological lesions in nephropathology. This likely can be extended to automatically quantify all relevant lesions for a kidney transplant, such as Banff lesions. Important limitations and challenges exist in the collection of representative data sets and the updates of Banff classification, making large-scale studies challenging. The already positive study results make future AI support in kidney transplant pathology appear likely.

Dynamic monitoring soft tissue healing via visualized Gd-crosslinked double network MRI microspheres

By integrating magnetic resonance-visible components with scaffold materials, hydrogel microspheres (HMs) become visible under magnetic resonance imaging(MRI), allowing for non-invasive, continuous, and dynamic monitoring of the distribution, degradation, and relationship of the HMs with local tissues. However, when these visualization components are physically blended into the HMs, it reduces their relaxation rate and specificity under MRI, weakening the efficacy of real-time dynamic monitoring. To achieve MRI-guided in vivo monitoring of HMs with tissue repair functionality, we utilized airflow control and photo-crosslinking methods to prepare alginate-gelatin-based dual-network hydrogel microspheres (G-AlgMA HMs) using gadolinium ions (Gd (III)), a paramagnetic MRI contrast agent, as the crosslinker. When the network of G-AlgMA HMs degrades, the cleavage of covalent bonds causes the release of Gd (III), continuously altering the arrangement and movement characteristics of surrounding water molecules. This change in local transverse and longitudinal relaxation times results in variations in MRI signal values, thus enabling MRI-guided in vivo monitoring of the HMs. Additionally, in vivo data show that the degradation and release of polypeptide (K2 (SL)6 K2 (KK)) from G-AlgMA HMs promote local vascular regeneration and soft tissue repair. Overall, G-AlgMA HMs enable non-invasive, dynamic in vivo monitoring of biomaterial degradation and tissue regeneration through MRI, which is significant for understanding material degradation mechanisms, evaluating biocompatibility, and optimizing material design.

Design, cohort profile and comparison of the KTD-Innov study: a prospective multidimensional biomarker validation study in kidney allograft rejection

There is an unmet need for robust and clinically validated biomarkers of kidney allograft rejection. Here we present the KTD-Innov study (ClinicalTrials.gov, NCT03582436), an unselected deeply phenotyped cohort of kidney transplant recipients with a holistic approach to validate the clinical utility of precision diagnostic biomarkers. In 2018-2019, we prospectively enrolled consecutive adult patients who received a kidney allograft at seven French centers and followed them for a year. We performed multimodal phenotyping at follow-up visits, by collecting clinical, biological, immunological, and histological parameters, and analyzing a panel of 147 blood, urinary and kidney tissue biomarkers. The primary outcome was allograft rejection, assessed at each visit according to the international Banff 2019 classification. We evaluated the representativeness of participants by comparing them with patients from French, European, and American transplant programs transplanted during the same period. A total of 733 kidney transplant recipients (64.1% male and 35.9% female) were included during the study. The median follow-up after transplantation was 12.3 months (interquartile range, 11.9-13.1 months). The cumulative incidence of rejection was 9.7% at one year post-transplant. We developed a distributed and secured data repository in compliance with the general data protection regulation. We established a multimodal biomarker biobank of 16,736 samples, including 9331 blood, 4425 urinary and 2980 kidney tissue samples, managed and secured in a collaborative network involving 7 clinical centers, 4 analytical platforms and 2 industrial partners. Patients' characteristics, immune profiles and treatments closely resembled those of 41,238 French, European and American kidney transplant recipients. The KTD-Innov study is a unique holistic and multidimensional biomarker validation cohort of kidney transplant recipients representative of the real-world transplant population. Future findings from this cohort are likely to be robust and generalizable.

Utility of Routine Post Kidney Transplant Anti-HLA Antibody Screening

Introduction

De novo donor-specific antibody (dnDSA) is a strong biomarker associated with the development of antibody-mediated rejection (AMR) and graft loss after kidney transplantation. This procedure is expensive; however, systematic annual screening was recommended by some national organ transplant agencies or societies even though its clinical utility was not clearly established.

Methods

To address this question, we retrospectively assessed the incidence of dnDSA according to the test justification (clinically indicated or systematic) in a cohort of low-immunological risk patients, defined by being nonhuman leukocyte antigen (non-HLA)-sensitized and having no previous kidney transplants.

Results

A total of 1072 patients, for whom 4611 anti-HLA tests were performed, were included in the study. During the follow-up period of 8 (interquartile range, IQR: 5-11) years, 77 recipients developed dnDSA (prevalence of 7.2%). Thirty-five of these dnDSAs (45.5%) were detected during the first year posttransplantation. In 95% of patients with dnDSA, an immunizing event was identified in their medical records. dnDSA was detected in 46 of 4267 systematic screening tests (1.08%) performed. Active and chronic AMR were frequently observed in biopsies performed after systematic DSA testing (17.9% and 15.4%, respectively).

Conclusion

Our results suggest that the detection by systematic screening of dnDSA in low-immunological risk kidney transplant patients without sensitizing events is a rare event, especially after 1 year. Moreover, in real life, systematic annual screening for dnDSA, seems having a limited impact to detect AMR at an earlier stage compared to patients in whom dnDSA was detected after a clinically indicated test.

Biopsy-based transcriptomics in the diagnosis of kidney transplant rejection

PURPOSE OF REVIEW

The last year has seen considerable progress in translational research exploring the clinical utility of biopsy-based transcriptomics of kidney transplant biopsies to enhance the diagnosis of rejection. This review will summarize recent findings with a focus on different platforms, potential clinical applications, and barriers to clinical adoption.

RECENT FINDINGS

Recent literature has focussed on using biopsy-based transcriptomics to improve diagnosis of rejection, in particular antibody-mediated rejection. Different techniques of gene expression analysis (reverse transcriptase quantitative PCR, microarrays, probe-based techniques) have been used either on separate samples with ideally preserved RNA, or on left over tissue from routine biopsy processing. Despite remarkable consistency in overall patterns of gene expression, there is no consensus on acceptable indications, or whether biopsy-based transcriptomics adds significant value at reasonable cost to current diagnostic practice.

SUMMARY

Access to biopsy-based transcriptomics will widen as regulatory approvals for platforms and gene expression models develop. Clinicians need more evidence and guidance to inform decisions on how to use precious biopsy samples for biopsy-based transcriptomics, and how to integrate results with standard histology-based diagnosis.

Deep learning applications for kidney histology analysis

PURPOSE OF REVIEW

Nephropathology is increasingly incorporating computational methods to enhance research and diagnostic accuracy. The widespread adoption of digital pathology, coupled with advancements in deep learning, will likely transform our pathology practices. Here, we discuss basic concepts of deep learning, recent applications in nephropathology, current challenges in implementation and future perspectives.

RECENT FINDINGS

Deep learning models have been developed and tested in various areas of nephropathology, for example, predicting kidney disease progression or diagnosing diseases based on imaging and clinical data. Despite their promising potential, challenges remain that hinder a wider adoption, for example, the lack of prospective evidence and testing in real-world scenarios.

SUMMARY

Deep learning offers great opportunities to improve quantitative and qualitative kidney histology analysis for research and clinical nephropathology diagnostics. Although exciting approaches already exist, the potential of deep learning in nephropathology is only at its beginning and we can expect much more to come.

Electro-Optical Multiclassification Platform for Minimizing Occasional Inaccuracy in Point-of-Care Biomarker Detection

On-site diagnostic tests that accurately identify disease biomarkers lay the foundation for self-healthcare applications. However, these tests routinely rely on single-mode signals and suffer from insufficient accuracy, especially for multiplexed point-of-care tests (POCTs) within a few minutes. Here, this work develops a dual-mode multiclassification diagnostic platform that integrates an electrochemiluminescence sensor and a field-effect transistor sensor in a microfluidic chip. The microfluidic channel guides the testing samples to flow across electro-optical sensor units, which produce dual-mode readouts by detecting infectious biomarkers of tuberculosis (TB), human rhinovirus (HRV), and group B streptococcus (GBS). Then, machine-learning classifiers generate three-dimensional (3D) hyperplanes to diagnose different diseases. Dual-mode readouts derived from distinct mechanisms enhance the anti-interference ability physically, and machine-learning-aided diagnosis in high-dimensional space reduces the occasional inaccuracy mathematically. Clinical validation studies with 501 unprocessed samples indicate that the platform has an accuracy approaching 99%, higher than the 77%-93% accuracy of rapid point-of-care testing technologies at 100% statistical power (>150 clinical tests). Moreover, the diagnosis time is 5 min without a trade-off of accuracy. This work solves the occasional inaccuracy issue of rapid on-site diagnosis, endowing POCT systems with the same accuracy as laboratory tests and holding unique prospects for complicated scenes of personalized healthcare.

Emerging phenotypes in kidney transplant rejection

PURPOSE OF REVIEW

This review focuses on more recently emerging rejection phenotypes in the context of time post transplantation and the resulting differential diagnostic challenges. It also discusses how novel ancillary diagnostic tools can potentially increase the accuracy of biopsy-based rejection diagnosis.

RECENT FINDINGS

With advances in reducing immunological risk at transplantation and improved immunosuppression treatment renal allograft survival improved. However, allograft rejection remains a major challenge and represent a frequent course for allograft failure. With prolonged allograft survival, novel phenotypes of rejection are emerging, which can show complex overlap and transition between cellular and antibody-mediated rejection mechanisms as well as mixtures of acute/active and chronic diseases. With the emerging complexity in rejection phenotypes, it is crucial to achieve diagnostic accuracy in the individual patient.

SUMMARY

The prospective validation and adoption of novel molecular and computational diagnostic tools into well defined and appropriate clinical context of uses will improve our ability to accurately diagnose, stage, and grade allograft rejection.

Exploring the single-cell immune landscape of kidney allograft inflammation using imaging mass cytometry

Kidney allograft inflammation, mostly attributed to rejection and infection, is an important cause of graft injury and loss. Standard histopathological assessment of allograft inflammation provides limited insights into biological processes and the immune landscape. Here, using imaging mass cytometry with a panel of 28 validated biomarkers, we explored the single-cell landscape of kidney allograft inflammation in 32 kidney transplant biopsies and 247 high-dimensional histopathology images of various phenotypes of allograft inflammation (antibody-mediated rejection, T cell-mediated rejection, BK nephropathy, and chronic pyelonephritis). Using novel analytical tools, for cell segmentation, we segmented over 900 000 cells and developed a tissue-based classifier using over 3000 manually annotated kidney microstructures (glomeruli, tubules, interstitium, and arteries). Using PhenoGraph, we identified 11 immune and 9 nonimmune clusters and found a high prevalence of memory T cell and macrophage-enriched immune populations across phenotypes. Additionally, we trained a machine learning classifier to identify spatial biomarkers that could discriminate between the different allograft inflammatory phenotypes. Further validation of imaging mass cytometry in larger cohorts and with more biomarkers will likely help interrogate kidney allograft inflammation in more depth than has been possible to date.

The transformative potential of artificial intelligence in solid organ transplantation

Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.

The Banff 2022 Kidney Meeting Report: Reappraisal of microvascular inflammation and the role of biopsy-based transcript diagnostics

The XVI-th Banff Meeting for Allograft Pathology was held at Banff, Alberta, Canada, from 19th to 23rd September 2022, as a joint meeting with the Canadian Society of Transplantation. To mark the 30th anniversary of the first Banff Classification, premeeting discussions were held on the past, present, and future of the Banff Classification. This report is a summary of the meeting highlights that were most important in terms of their effect on the Classification, including discussions around microvascular inflammation and biopsy-based transcript analysis for diagnosis. In a postmeeting survey, agreement was reached on the delineation of the following phenotypes: (1) "Probable antibody-mediated rejection (AMR)," which represents donor-specific antibodies (DSA)-positive cases with some histologic features of AMR but below current thresholds for a definitive AMR diagnosis; and (2) "Microvascular inflammation, DSA-negative and C4d-negative," a phenotype of unclear cause requiring further study, which represents cases with microvascular inflammation not explained by DSA. Although biopsy-based transcript diagnostics are considered promising and remain an integral part of the Banff Classification (limited to diagnosis of AMR), further work needs to be done to agree on the exact classifiers, thresholds, and clinical context of use.

Fluorescence spectroscopic profiling of urine samples for predicting kidney transplant rejection

Rejection is the primary factor affecting the functionality of a kidney post-transplant, where its prompt prediction of risk significantly influences therapeutic strategies and clinical outcomes. Current graft health assessment methods, including serum creatinine measurements and transplant kidney puncture biopsies, possess considerable limitations. In contrast, urine serves as a direct indicator of the graft's degenerative stage and provides a more accurate measure than peripheral blood analysis, given its non-invasive collection of kidney-specific metabolite. This research entailed collecting fluorescent fingerprint data from 120 urine samples of post-renal transplant patients using hyperspectral imaging, followed by the development of a learning model to detect various forms of immunological rejection. The model successfully identified multiple rejection types with an average diagnostic accuracy of 95.56 %.Beyond proposing an innovative approach for predicting the risk of complications post-kidney transplantation, this study heralds the potential introduction of a non-invasive, rapid, and accurate supplementary method for risk assessment in clinical practice.

Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

The uptake of [18F]-fluorodeoxyglucose by the renal allograft correlates with the acute Banff scores of cortex inflammation but not with the 1-year graft outcomes

Introduction

[18F]FDG PET/CT noninvasively disproves acute kidney allograft rejection (AR) in kidney transplant recipients (KTRs) with suspected AR. However, the correlation of biopsy-based Banff vs. PET/CT-based scores of acute inflammation remains unknown, as does the prognostic performance of [18F]FDG PET/CT at one year post suspected AR.

Methods

From 2012 to 2019, 114 [18F]FDG-PET/CTs were prospectively performed in 105 adult KTRs who underwent per cause transplant biopsies. Ordinal logistic regression assessed the correlation between the extent of histological inflammation and the mean standardized [18F]FDG uptake values (mSUVmean). Functional outcomes of kidney allografts were evaluated at one year post per cause biopsy and correlated to mSUVmean.

Results

A significant correlation between mSUVmean and acute Banff score was found, with an adjusted R 2 of 0.25. The mSUVmean was significantly different between subgroups of "total i", with 2.30 ± 0.71 in score 3 vs. 1.68 ± 0.24 in score 0. Neither the function nor the survival of the graft at one year was statistically related to mSUVmean.

Discussion

[18F]FDG-PET/CT may help noninvasively assess the severity of kidney allograft inflammation in KTRs with suspected AR, but it does not predict graft outcomes at one year.

Pathophysiology of Rejection in Kidney Transplantation

Kidney transplantation has been the optimal treatment for end-stage kidney disease for almost 70 years, with increasing frequency over this period. Despite the prevalence of the procedure, allograft rejection continues to impact transplant recipients, with consequences ranging from hospitalization to allograft failure. Rates of rejection have declined over time, which has been largely attributed to developments in immunosuppressive therapy, understanding of the immune system, and monitoring. Developments in these therapies, as well as an improved understanding of rejection risk and the epidemiology of rejection, are dependent on a foundational understanding of the pathophysiology of rejection. This review explains the interconnected mechanisms behind antibody-mediated and T-cell-mediated rejection and highlights how these processes contribute to outcomes and can inform future progress.

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.