Evidence map›Paper›PMID 37794720›Full record

ArticleThe Journal of pathology2023

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

Steven Hart, Victor Garcia, Sarah N Dudgeon, Matthew G Hanna, Xiaoxian Li, Kim Rm Blenman, Katherine Elfer, Amy Ly, Roberto Salgado, Joel Saltz and 5 more

Open access · hybridAbstract read
In one paragraph

Article in The Journal of pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 10 institutions in 3 countries.

Steven HartDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0001-7714-2734
Victor GarciaDivision of Imaging, Diagnostics, and Software Reliability, Office Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA.ORCID 0000-0003-4507-3839
Sarah N DudgeonComputational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA.
Matthew G HannaMemorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID 0000-0002-7536-1746
Xiaoxian LiDepartment of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.
Kim Rm BlenmanDepartment of Internal Medicine, Section of Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA.ORCID 0000-0001-6070-0728
Katherine ElferDivision of Imaging, Diagnostics, and Software Reliability, Office Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA.
Amy LyDepartment of Pathology, Massachusetts General Hospital, Boston, MA, USA.
Roberto SalgadoDepartment of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium.
Joel SaltzDepartment of Biomedical Informatics, Stony Brook School of Medicine, Stony Brook, NY, USA.ORCID 0000-0002-3451-2165
Rajarsi GuptaDepartment of Biomedical Informatics, Stony Brook School of Medicine, Stony Brook, NY, USA.ORCID 0000-0002-1577-8718
Evangelos HytopoulosiRhythm Technologies Inc., San Francisco, CA, USA.
Denis LarsimontDepartment of Pathology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium.
Jochen LennerzMassachusetts General Hospital/Massachusetts General Hospital, Center for Integrated Diagnostics, Boston, MA, USA.
Brandon D GallasDivision of Imaging, Diagnostics, and Software Reliability, Office Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA.ORCID 0000-0001-7332-1620
United States Food and Drug Administration · USMassachusetts General Hospital · USStony Brook School · USYale University · USEmory University · USiRhythm (United States) · USMayo Clinic in Arizona · USMemorial Sloan Kettering Cancer Center · USPeter MacCallum Cancer Centre · AUUniversité Libre de Bruxelles · BE

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Yale-Mayo FDA Center for Excellence in Regulatory Science and InnovationU01FD005938 · FDA · YALE UNIVERSITY · PI Joseph Solomon Ross · 2016 to 2026
$25.4M
FDA HHS FD005938FDA HHS U01 FD005938Intramural FDA HHS FD999999NCATS NIH HHS UL1 TR001863
6 · The paper itself

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.

Indexed as

Lymphocytes, Tumor-InfiltratingPathologistsHumansUnited KingdomUnited StatesUnited States Food and Drug Administrationartificial intelligencecomputational pathologymachine learningmedical device development toolmodel validationregulatory sciencetriple-negative breast cancertumor-infiltrating lymphocytes

Identifiers

PMID37794720
PMCPMC10841854
OpenAlexW4387357945

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.