Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection

Allograft rejection remains a significant concern following all solid organ transplants. While qualitative morphologic analysis with histologic grading of biopsy samples is the main tool employed, for diagnosing allograft rejection, this standard has significant limitations in precision and accuracy that affect patient care. The use of endomyocardial biopsy (EMB) to diagnose cardiac allograft rejection (CAR) illustrates the significant shortcomings of current approaches for diagnosing allograft rejection. Despite disappointing interobserver variability, concerns about discordance with clinical trajectories, attempts at revising the histologic criteria and efforts to establish new diagnostic tools with imaging and gene expression profiling, no method has yet supplanted EMB as the diagnostic gold standard. In this context, automated approaches to complex data analysis problems – often referred to as 'machine learning' – represent promising strategies to improve overall diagnostic accuracy. By focusing on cardiac allograft rejection, where tissue sampling is relatively frequent, this review highlights the limitations of the current approach to diagnosing allograft rejection, introduces the basic methodology behind machine learning and automated image feature detection, and highlights the initial successes of these approaches within cardiovascular medicine. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Corresponding Author: Kenneth B. Margulies, M.D., Perelman School of Medicine, University of Pennsylvania, Translational Research Center, Room 11–101, 3400 Civic Center Boulevard, Building 421, Philadelphia, PA 19104, USA. Tel.: + 1 215 573 2980; fax: + 1 215 898 3473. Email: ken.margulies@uphs.upenn.edu Author Information: Eliot G. Peyster – primary author, participated in writing Anant Madabhushi – participated in writing Kenneth B. Margulies – participated in writing, corresponding author Author Disclosures: Dr. Peyster has nothing to disclose. Dr. Madabhushi is the cofounder and stakeholder in Ibris Inc., a cancer diagnostics company. Dr. Madabhushi is an equity holder and has technology licensed to both Elucid Bioimaging and Inspirata Inc. Dr. Madabhushi is a scientific advisory consultant for Inspirata Inc. Part of his research work has been sponsored by Philips Healthcare and he has NIH funded projects in collaboration with PathCore Inc. and Inspirata. He also sits on the Tumor Modeling advisory board at Astrazeneca Inc and at Inspirata Inc. Dr. Margulies holds research grants from Thoratec Corporation, Merck and Glaxo-Smith-Kline, and serves as a scientific consultant/advisory board member for Janssen, Merck, Pfizer Glaxo-Smith-Kline. Funding: This publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number TL1TR001880, the National Cancer Institute of the National Institutes of Health under award numbers (R21CA179327-01, R21CA195152-01, U24CA199374-01, the National Institute of Diabetes and Digestive and Kidney Diseases under award number R01DK098503-02, the National Heart Lung and Blood Institute under award number R01-HL105993, the DOD Prostate Cancer Synergistic Idea Development Award (PC120857); the DOD Lung Cancer Idea Development New Investigator Award (LC130463), the DOD Prostate Cancer Idea Development Award; the Case Comprehensive Cancer Center Pilot Grant, the VelaSano Grant from the Cleveland Clinic the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University, the I-Corps@Ohio Program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other grant providers. Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.

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