Evidence map›Paper›PMID 36865216›Full record

ArticlebioRxiv : the preprint server for biology2023

DeepHeme: A generalizable, bone marrow classifier with hematopathologist-level performance.

Gregory M Goldgof, Shenghuan Sun, Jacob Van Cleave, Linlin Wang, Fabienne Lucas, Laura Brown, Jacob D Spector, Leonardo Boiocchi, Jeeyeon Baik, Menglei Zhu and 7 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

17 authors.

Gregory M GoldgofDepartment of Laboratory Medicine, University of California, San Francisco, CA, USA.
Shenghuan SunBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
Jacob Van CleaveDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Linlin WangDepartment of Laboratory Medicine, University of California, San Francisco, CA, USA.
Fabienne LucasDepartment of Pathology, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA.
Laura BrownDepartment of Laboratory Medicine, University of California, San Francisco, CA, USA.
Jacob D SpectorDepartment of Laboratory Medicine, Boston Children's Hospital/Harvard Medical School, Boston, MA, USA.
Leonardo BoiocchiDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Jeeyeon BaikDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Menglei ZhuDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Orly ArdonDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Chuanyi M LuDepartment of Laboratory Medicine, University of California, San Francisco, CA, USA.
Ahmet DoganDepartment of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Dmitry B GoldgofDepartment of Computer Science, University of South Florida, Tampa, FL, USA.
Iain CarmichaelDepartment of Statistics, University of California, Berkeley, CA, USA.
Sonam PrakashDepartment of Laboratory Medicine, University of California, San Francisco, CA, USA.
Atul J ButteBakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.

Funding

Enhancing Resident Investigation and Career Advancement in Cardiovascular and Pulmonary ScienceR38HL143581 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BIBBINS-DOMINGO, KIRSTEN, HUANG, ALISON · 2018 to 2021
$1.4M
Deep learning tools for the automated analysis of hematopathology whole slide images and the development of prognostic algorithms for hematopoietic stem cell transplant recipientsK38HL159128 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GOLDGOF, GREGORY MARK · 2021 to 2021
$119k
NHLBI NIH HHS K38 HL159128NHLBI NIH HHS R38 HL143581
6 · The paper itself

Abstract

Morphology-based classification of cells in the bone marrow aspirate (BMA) is a key step in the diagnosis and management of hematologic malignancies. However, it is time-intensive and must be performed by expert hematopathologists and laboratory professionals. We curated a large, high-quality dataset of 41,595 hematopathologist consensus-annotated single-cell images extracted from BMA whole slide images (WSIs) containing 23 morphologic classes from the clinical archives of the University of California, San Francisco. We trained a convolutional neural network, DeepHeme, to classify images in this dataset, achieving a mean area under the curve (AUC) of 0.99. DeepHeme was then externally validated on WSIs from Memorial Sloan Kettering Cancer Center, with a similar AUC of 0.98, demonstrating robust generalization. When compared to individual hematopathologists from three different top academic medical centers, the algorithm outperformed all three. Finally, DeepHeme reliably identified cell states such as mitosis, paving the way for image-based quantification of mitotic index in a cell-specific manner, which may have important clinical applications.

Identifiers

PMID36865216
PMCPMC9979993

What OpenQuestion holds

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Registered trials

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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.