Format

Send to

Choose Destination
J Pathol. 2019 Nov;249(3):286-294. doi: 10.1002/path.5331. Epub 2019 Sep 3.

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.

Author information

1
Regulatory and Clinical Affairs, PathAI, Boston, MA, USA.
2
Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
3
Amgen Research, Comparative Biology and Safety Sciences, Amgen Inc., South San Francisco, CA, USA.
4
Department of Pathology and Laboratory Medicine, Drexel University College of Medicine, Philadelphia, PA, USA.
5
Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
6
Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
7
Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA.
8
Data Science Department, Chan Zuckerberg Biohub, San Francisco, CA, USA.
9
Department of Pathology, The Ohio State University, Columbus, OH, USA.
10
Hyman, Phelps & McNamara, P.C, Washington, DC, USA.
11
PathAI, Boston, MA, USA.
12
Department of Development Sciences, Genentech Inc., South San Francisco, CA, USA.

Abstract

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber-security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

KEYWORDS:

artificial intelligence; computational pathology; convolutional neural networks; deep learning; digital pathology; image analysis; machine learning

PMID:
31355445
DOI:
10.1002/path.5331

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center