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

BACKGROUND: In the setting of kidney transplantation, histopathology of kidney biopsies is a key element in the organ assessment and allocation. Despite the broad diffusion of the Remuzzi–Karpinski score on preimplantation kidney biopsies, scientific evidence of its correlation to the transplantation outcome is controversial. The main issues affecting the prognostic value of histopathology are the referral to general on-call pathologists and the semiquantitative feature of the score, which can raise issues of interpretation. Digital pathology has shown very reliable and effective in the oncological diagnosis and treatment; however, the spread of such technologies is lagging behind in the field of transplantation. The aim of our study was to create a digital online platform where whole-slide images (WSI) of preimplantation kidney biopsies could be uploaded and stored. METHODS: We included 210 kidney biopsies collected between January 2015 and December 2019 from the joint collaboration of the transplantation centers of Padua and Verona. The selected slides, stained with hematoxylin and eosin, were digitized and uploaded on a shared web platform. For each case, the on-call pathologists' Remuzzi grades were obtained from the original report, together with the clinical data and the posttransplantation follow-up. RESULTS: The storage of WSI of preimplantation kidney biopsies would have several clinical, scientific, and educational advantages. The clinical utility relies on the possibility to consult online expert pathologists and real-time quality checks of diagnosis. From the perspective of follow-up, the archived digitized biopsies can offer a useful comparison to posttransplantation biopsies. In addition, the digital online platform is a precious tool for multidisciplinary meetings aimed both at the clinical discussion and at the design of research projects. Furthermore, this archive of readily available WSI is an important educational resource for the training of professionals. CONCLUSIONS: Finally, the web platform lays the foundation for the introduction of artificial intelligence in the field of transplantation that would help create new diagnostic algorithms and tools with the final aim of increasing the precision of organ assessment and its predictive value for transplant outcome.