A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients

Hetal Desai Marble; Richard Huang; Sarah Nixon Dudgeon; Amanda Lowe; Markus D Herrmann; Scott Blakely; Matthew O Leavitt; Mike Isaacs; Matthew G Hanna; Ashish Sharma; Jithesh Veetil; Pamela Goldberg; Joachim H Schmid; Laura Lasiter; Brandon D Gallas; Esther Abels; Jochen K Lennerz

doi:10.4103/jpi.jpi_27_20

A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients

J Pathol Inform. 2020 Aug 6:11:22. doi: 10.4103/jpi.jpi_27_20. eCollection 2020.

Authors

Hetal Desai Marble¹, Richard Huang¹, Sarah Nixon Dudgeon², Amanda Lowe³, Markus D Herrmann⁴, Scott Blakely⁵, Matthew O Leavitt⁶, Mike Isaacs⁷, Matthew G Hanna⁸, Ashish Sharma⁹, Jithesh Veetil¹⁰, Pamela Goldberg¹⁰, Joachim H Schmid¹¹, Laura Lasiter¹², Brandon D Gallas², Esther Abels¹³, Jochen K Lennerz¹

Affiliations

¹ Department of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.
² Division of Imaging, Diagnostics, and Software Reliability, Center for Devices and Radiological Health, Food and Drug Administration, Office of Science and Engineering Laboratories, Silver Spring, MD, USA.
³ Visiopharm Americas, Westminster, CO, USA.
⁴ Department of Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.
⁵ Hamamatsu Corporation, Pittsburgh, PA, USA.
⁶ LUMEA, Salt Lake City, UT, USA.
⁷ Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
⁸ Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁹ Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA.
¹⁰ Medical Device Innovation Consortium, Arlington, VA, USA.
¹¹ Roche Tissue Diagnostics, Santa Clara, USA.
¹² Friends of Cancer Research, Washington, DC, USA.
¹³ PathAI, Boston, MA, USA.

Abstract

Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology (the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.

Keywords: Artificial intelligence; digital pathology; machine learning; regulatory science; slide scanning.