Informatics approaches to address new challenges in the classification of lymphoid malignancies

• Type of Material: Article
• Authors: Jacob Jordan, Emory University School of Medicine; Jordan Goldstein, Emory University School of Medicine; David Jaye, Emory University; Metin Gurcan, The Ohio State University; Christopher Flowers, Emory University; Lee Cooper, Emory University
• Language: English
• Date: 2018-01-01
• Publisher: American Society of Clinical Oncology
• Publication Version: Final Published Version
• Copyright Statement: © 2018 American Society of Clinical Oncology.
• Final Published Version (URL): http://dx.doi.org/10.1200/CCI.17.00039
• Title of Journal or Parent Work: JCO Clinical Cancer Informatics
• Volume: 2018
• Issue: 2
• Start Page: 1
• End Page: 9
• Grant/Funding Information: Supported by National Institutes of Health Grant 3U01CA195568-02S2 (J.J.) and National Cancer Institute Grants 5U24CA194362 (L.A.D.C) and 5U24CA199374 (M.G.).
• Keywords: World Health Organization; Algorithms; Lymphoma; Image Processing, Computer-Assisted; Immunohistochemistry; Neoplasm Grading; Medical Informatics; Lymphoma, Follicular; Humans
• Research Categories: Health Sciences, Medicine and Surgery; Health Sciences, Oncology

Abstract

Purpose Lymphoid malignancies are remarkably heterogeneous, with variations in outcomes and clinical, biologic, and histologic presentation complicating classification according to the World Health Organization guidelines. Incorrect classification of lymphoid neoplasms can result in suboptimal therapeutic strategies for individual patients and confound the interpretation of clinical trials involving personalized, class-based treatments. This review discusses the potential role of pathology informatics in improving the classification accuracy and objectivity for lymphoid malignancies. Design We identified peer-reviewed publications examining pathology informatics approaches for the classification of lymphoid malignancies, reviewed developments in the lymphoma classification systems, and summarized computational methods for pathologic assessment that can impact practice. Results Computer-assisted pathology image analysis algorithms in lymphoma most commonly have been applied to follicular lymphoma to address biologic heterogeneity and subjectivity in the process of classification. Conclusion Objective methods are available to assist pathologists in lymphoma classification and grading, and have been demonstrated to provide measurable benefits in specific contexts. Future validation and extension of these approaches will require datasets that link high resolution pathology images available for image analysis algorithms with clinical variables and follow up outcomes.