Allograft tissue under the microscope: only the beginning

Author:

Virmani Sarthak,Rao Arundati,Menon Madhav C.

Abstract

Purpose of review To review novel modalities for interrogating a kidney allograft biopsy to complement the current Banff schema. Recent findings Newer approaches of Artificial Intelligence (AI), Machine Learning (ML), digital pathology including Ex Vivo Microscopy, evaluation of the biopsy gene expression using bulk, single cell, and spatial transcriptomics and spatial proteomics are now available for tissue interrogation. Summary Banff Schema of classification of allograft histology has standardized reporting of tissue pathology internationally greatly impacting clinical care and research. Inherent sampling error of biopsies, and lack of automated morphometric analysis with ordinal outputs limit its performance in prognostication of allograft health. Over the last decade, there has been an explosion of newer methods of evaluation of allograft tissue under the microscope. Digital pathology along with the application of AI and ML algorithms could revolutionize histopathological analyses. Novel molecular diagnostics such as spatially resolved single cell transcriptomics are identifying newer mechanisms underlying the pathologic diagnosis to delineate pathways of immunological activation, tissue injury, repair, and regeneration in allograft tissues. While these techniques are the future of tissue analysis, costs and complex logistics currently limit their clinical use.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Transplantation,Immunology and Allergy

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. In Silico Optimization of Tissue Microarray Design for Machine Learning Analysis;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27

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