Initial interactions with the FDA on developing a validation dataset as a medical device development tool Editorial


Authors: Hart, S.; Garcia, V.; Dudgeon, S. N.; Hanna, M. G.; Li, X.; Blenman, K. R. M.; Elfer, K.; Ly, A.; Salgado, R.; Saltz, J.; Gupta, R.; Hytopoulos, E.; Larsimont, D.; Lennerz, J.; Gallas, B. D.
Title: Initial interactions with the FDA on developing a validation dataset as a medical device development tool
Abstract: Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
Keywords: united states; united states food and drug administration; tumor associated leukocyte; lymphocytes, tumor-infiltrating; food and drug administration; pathology; artificial intelligence; united kingdom; pathologist; tumor-infiltrating lymphocytes; triple-negative breast cancer; pathologists; machine learning; humans; human; model validation; computational pathology; regulatory science; medical device development tool
Journal Title: Journal of Pathology
Volume: 261
Issue: 4
ISSN: 0022-3417
Publisher: Wiley Blackwell  
Date Published: 2023-12-01
Start Page: 378
End Page: 384
Language: English
DOI: 10.1002/path.6208
PUBMED: 37794720
PROVIDER: scopus
DOI/URL:
Notes: Article -- Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. Matthew George Hanna
    101 Hanna