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

Jacob Jordan, Jordan S. Goldstein, David L. Jaye, Metin Gurcan, Christopher R. Flowers, Lee Alex Donald Cooper

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
JournalJCO Clinical Cancer Informatics
Volume2018
Issue number2
DOIs
StatePublished - 2018

ASJC Scopus subject areas

  • Cancer Research
  • Oncology
  • Health Informatics

Fingerprint Dive into the research topics of 'Informatics approaches to address new challenges in the classification of lymphoid malignancies'. Together they form a unique fingerprint.

Cite this