Evidence map›Paper›PMID 33042601›Full record

ArticleJournal of pathology informatics2020

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 and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pathology informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
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

20 citing papers in PubMed.

  1. Regulatory science for AI-basedJournal of pathology informatics · 2026
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  8. Article
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  13. Quality control stress test for deep learning-based diagnostic model in digital pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2021
    Article
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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

17 authors.

Hetal Desai MarbleDepartment of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.
Richard HuangDepartment of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.
Sarah Nixon DudgeonDivision 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.
Amanda LoweVisiopharm Americas, Westminster, CO, USA.
Markus D HerrmannDepartment of Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.
Scott BlakelyHamamatsu Corporation, Pittsburgh, PA, USA.
Matthew O LeavittLUMEA, Salt Lake City, UT, USA.
Mike IsaacsDepartment of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
Matthew G HannaDepartment of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Ashish SharmaDepartment of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA.
Jithesh VeetilMedical Device Innovation Consortium, Arlington, VA, USA.
Pamela GoldbergMedical Device Innovation Consortium, Arlington, VA, USA.
Joachim H SchmidRoche Tissue Diagnostics, Santa Clara, USA.
Laura LasiterFriends of Cancer Research, Washington, DC, USA.
Brandon D GallasDivision 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.
Esther AbelsPathAI, Boston, MA, USA.
Jochen K LennerzDepartment of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.

Funding

Illuminating the evolutionary history of colorectal cancer metastasis: basic principles and clinical applicationsR37CA225655 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI NAXEROVA, KAMILA · 2018 to 2024
$3.0M
NCI NIH HHS R37 CA225655
6 · The paper itself

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

Indexed as

Artificial intelligencedigital pathologymachine learningregulatory scienceslide scanning

Identifiers

PMID33042601
PMCPMC7518200

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.