Evidence map›Paper›PMID 38433289›Full record

ReviewHistopathology2024

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.

Amy Ly, Victor Garcia, Kim R M Blenman, Anna Ehinger, Katherine Elfer, Matthew G Hanna, Xiaoxian Li, Dieter J E Peeters, Ryan Birmingham, Sarah Dudgeon and 11 more

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Histopathology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

10 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Observational
  6. Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025
    Review
  7. Review
  8. Article
  9. Targeting low-risk triple-negative breast cancer: a review on de-escalation strategies for a new era.Translational breast cancer research : a journal focusing on translational research in breast cancer · 2025
    Review
  10. 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

21 authors at 13 institutions in 6 countries.

Amy LyDepartment of Pathology, Massachusetts General Hospital, Boston, MA, USA.ORCID https://orcid.org/0000-0002-3889-1123
Victor GarciaCenter for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA.
Kim R M BlenmanDepartment of Internal Medicine, Section of Medical Oncology and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
Anna EhingerDepartment of Genetics, Pathology and Molecular Diagnostics, Laboratory Medicine, Region Skane, Lund University, Lund, Sweden.ORCID https://orcid.org/0000-0001-9225-7396
Katherine ElferCenter for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA.
Matthew G HannaDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Xiaoxian LiDepartment of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.
Dieter J E PeetersDepartment of Pathology, University Hospital Antwerp, Edegem, Belgium.ORCID https://orcid.org/0000-0002-6943-2675
Ryan BirminghamCenter for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA.
Sarah DudgeonCenter for Computational Health, Yale School of Medicine, New Haven, CT, USA.
Emma GardeckiCenter for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA.
Rajarsi GuptaDepartment of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
Jochen LennerzDepartment of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital, Boston, MA, USA.
Tony PanDepartment of Biomedical Informatics, Emory University, Atlanta, GA, USA.
Joel SaltzDepartment of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
Keith A WhartonRoche Diagnostic Solutions, Oro Valley, AZ, USA.
Daniel EhingerDepartment of Clinical Sciences, Division of Oncology, Lund University, Lund, Sweden.ORCID https://orcid.org/0000-0001-7873-8505
Balazs AcsDepartment of Oncology and Pathology, Cancer Centre Karolinska, Karolinksa Institutet, Stockholm, Sweden.
Elisabeth M C DequekerDepartment of Public Health and Primary Care, Biomedical Quality Assurance Research Unit, University of Leuven, Leuven, Belgium.
Roberto SalgadoDepartment of Pathology, Gasthuiszusters Antwerpen-Ziekenhuis Netwerk Antwerpen (GZA-ZNA) Hospitals, Antwerp, Belgium.
Brandon D GallasCenter for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA.ORCID https://orcid.org/0000-0001-7332-1620
United States Food and Drug Administration · USEmory University · USLund University · SEMassachusetts General Hospital · USStony Brook University · USAntwerp University Hospital · BEDepartment of Public Health · MMKarolinska University Hospital · SEMemorial Sloan Kettering Cancer Center · USPeter MacCallum Cancer Centre · AURoche (United States) · USYale Cancer Center · USYale New Haven Health System · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Intramural FDA HHS FD999999NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.

Indexed as

Breast NeoplasmsArtificial IntelligenceFemaleHumansLymphocytes, Tumor-InfiltratingPathologistsPrognosisbreast cancercontinuing medical educationFOAMedpathology educationtumour infiltrating lymphocytes

Identifiers

PMID38433289
PMCPMC10990791
OpenAlexW4392371860

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.