Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study

Background Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection.Methods We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance).Findings Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0.87 [ten times bootstrapped CI 0.85-0.88]) and disease (0.87 [0.86-0.88]), followed by a second CNN classifying biopsies classified as disease into rejection (0.75 [0.73-0.76]) and other diseases (0.75 [0.72-0.77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0.83 [0.80-0.85], disease 0.83 [0.73-0.91]; second CNN rejection 0.61 [0.51-0.70], other diseases 0.61 [0.50-4.74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0.80 [0.73-0.84], rejection 0.76 [0.66-0.80], other diseases 0.50 [0.36-0.57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium.Interpretation This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Immunopathology of vascular and renal diseases and of organ and celltransplantationIP

Guidelines for the clinical management of familial adenomatous polyposis (FAP).

Item does not contain fulltextBACKGROUND: Familial adenomatous polyposis (FAP) is a well-described inherited syndrome, which is responsible for <1% of all colorectal cancer (CRC) cases. The syndrome is characterised by the development of hundreds to thousands of adenomas in the colorectum. Almost all patients will develop CRC if they are not identified and treated at an early stage. The syndrome is inherited as an autosomal dominant trait and caused by mutations in the APC gene. Recently, a second gene has been identified that also gives rise to colonic adenomatous polyposis, although the phenotype is less severe than typical FAP. The gene is the MUTYH gene and the inheritance is autosomal recessive. In April 2006 and February 2007, a workshop was organised in Mallorca by European experts on hereditary gastrointestinal cancer aiming to establish guidelines for the clinical management of FAP and to initiate collaborative studies. Thirty-one experts from nine European countries participated in these workshops. Prior to the meeting, various participants examined the most important management issues according to the latest publications. A systematic literature search using Pubmed and reference lists of retrieved articles, and manual searches of relevant articles, was performed. During the workshop, all recommendations were discussed in detail. Because most of the studies that form the basis for the recommendations were descriptive and/or retrospective in nature, many of them were based on expert opinion. The guidelines described herein may be helpful in the appropriate management of FAP families. In order to improve the care of these families further, prospective controlled studies should be undertaken