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

Abstract

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

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

“…Yet, the long series of models developed to cope with this issue, including a variety of clinical and histologic variables, failed to predict long-term graft survival correctly [1]. In reality, however, the question is far more complex, and from an anatomical-pathological point of view, this fiasco could be explained by the lack of standards in indication [1,2], sampling, and diagnostic definition [3,4]. There is no perfect correlation between tissue alterations and functional manifestations but, on the other hand, histologic examination of kidney tissue provides a unique window of the organ that cannot be inferred from clinical records and renal functional studies all along.…”

“…A detailed, systematic literature review of the topic was performed to provide definitive evidence and expert opinion. The methodology allowed us to reach a solid consensus on various technical issues regarding the preimplantation kidney biopsy, and, at the moment, it represents the first attempt in Europe to standardize procedures with a multidisciplinary approach [3]. Telemedicine networks play a crucial role by leveraging technology to facilitate real-time exchange of views among the professionals (pathologists, nephrologists, and surgeons) involved in the decision-making process of kidney transplants [41].…”

Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software

“…In this context, two algorithms have been developed, known as RENFAST (Rapid Evaluation of Fibrosis And vessels Thickness) and RENTAG (Robust Evaluation of Tubular Atrophy and Glomerulosclerosis). RENFAST is designed for detecting vessels and interstitial fibrosis, while RENTAG focuses on detecting and classifying glomeruli and tubules [112,113]. Other researchers have also introduced similar semantic segmentation CNN models, albeit with slight variations.…”

“…Consequently, the accurate classification of glomerular conditions holds significant importance in the diagnosis of various kidney ailments. Moreover, these models have proven instrumental in the domain of transplant kidney biopsies, facilitating the detection of organ quality, the prediction of rejection risks and the diagnosis of other related conditions [112,114]. Many other CNN algorithms are applied in kidney histological investigations, such as U-Net algorithms that were shown to be particularly relevant for segmentation processes [115][116][117][118].…”