Evidence map›Paper›PMID 38043788›Full record

ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2024

Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.

Zhuoyu Wen, Danni Luo, Shidan Wang, Ruichen Rong, Bret M Evers, Liwei Jia, Yisheng Fang, Elena V Daoud, Shengjie Yang, Zifan Gu and 11 more

Open access · bronzeAbstract read
In one paragraph

Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed
12.0field-weighted citation impact, top 1% 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

66 citing papers in PubMed, 69 citations in OpenAlex.

  1. Review
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6 more citing papers are in PubMed but not listed here.

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 3 institutions in 1 country.

Zhuoyu WenQuantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas.
Danni LuoQuantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas.
Shidan WangQuantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas.
Ruichen RongQuantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas.
Bret M EversDepartment of Pathology, The University of Texas Southwestern Medical Center, Dallas, Texas.
Liwei JiaDepartment of Pathology, The University of Texas Southwestern Medical Center, Dallas, Texas.
Yisheng FangDepartment of Pathology, The University of Texas Southwestern Medical Center, Dallas, Texas.
Elena V DaoudDepartment of Pathology, The University of Texas Southwestern Medical Center, Dallas, Texas.
Shengjie YangQuantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas.
Zifan GuQuantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas.
Emily N ArnerDepartment of Surgery, The University of Texas Southwestern Medical Center, Dallas, Texas; Hamon Center for Therapeutic Oncology Research, The University of Texas Southwestern Medical Center, Dallas, Texas.
Cheryl M LewisDepartment of Pathology, The University of Texas Southwestern Medical Center, Dallas, Texas; Harold C. Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, Texas.
Luisa M Solis SotoDivision of Pathology and Laboratory Medicine, Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Junya FujimotoDivision of Pathology and Laboratory Medicine, Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Carmen BehrensDivision of Cancer Medicine, Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Ignacio I WistubaDivision of Pathology and Laboratory Medicine, Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Donghan M YangQuantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas.
Rolf A BrekkenDepartment of Surgery, The University of Texas Southwestern Medical Center, Dallas, Texas; Hamon Center for Therapeutic Oncology Research, The University of Texas Southwestern Medical Center, Dallas, Texas; Harold C. Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, Texas.
Kathryn A O'DonnellHarold C. Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, Texas; Hamon Center for Regenerative Medicine, The University of Texas Southwestern Medical Center, Dallas, Texas; Department of Molecular Biology, The University of Texas Southwestern Medical Center, Dallas, Texas.
Yang XieQuantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas; Hamon Center for Regenerative Medicine, The University of Texas Southwestern Medical Center, Dallas, Texas; Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, Texas.
Guanghua XiaoQuantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas; Hamon Center for Regenerative Medicine, The University of Texas Southwestern Medical Center, Dallas, Texas; Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address: Guanghua.Xiao@UTSouthwestern.edu.
The University of Texas Southwestern Medical Center · USThe University of Texas MD Anderson Cancer Center · USSouthwestern Medical Center · US

Funding

UNIVERSITY OF TEXAS--SPORE IN LUNG CANCERP50CA070907 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI GIBBONS, DON LYNN · 1996 to 2024
$57.4M
UT Southwestern Medical Center Simmons Comprehensive Cancer CenterP30CA142543 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Sandi Leigh Pruitt · 2010 to 2026
$53.7M
Deep Learning Image Analysis Algorithms to Improve Oral Cancer Risk Assessment for Oral Potentially Malignant DisordersR01DE030656 · NIDCR · YALE UNIVERSITY · PI PICKERING, CURTIS, XIAO, GUANGHUA · 2021 to 2025
$3.4M
Finding the optimal balance of immunotherapy efficacy and toxicity.U01AI156189 · NIAID · UT SOUTHWESTERN MEDICAL CENTER · PI GERBER, DAVID ERIC, WAKELAND, EDWARD K. · 2020 to 2024
$3.1M
Novel computational approaches to predict drug response and combination effectsR35GM136375 · NIGMS · UT SOUTHWESTERN MEDICAL CENTER · PI XIE, YANG · 2020 to 2024
$2.0M
The role of PCDH7 in lung cancer pathogenesisR01CA207763 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI O'DONNELL, KATHRYN ANN · 2017 to 2021
$2.0M
The role of translation initiation factor eIF5B in lung cancer pathogenesisR01CA273585 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Kathryn Ann O'Donnell · 2023 to 2026
$1.9M
Developing computational algorithms for histopathological image analysisR01GM140012 · NIGMS · UT SOUTHWESTERN MEDICAL CENTER · PI XIAO, GUANGHUA · 2021 to 2024
$1.6M
Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell LevelU01CA249245 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI XIAO, GUANGHUA · 2021 to 2023
$1.5M
Developing novel algorithms for spatial molecular profiling technologiesR01GM141519 · NIGMS · UT SOUTHWESTERN MEDICAL CENTER · PI XIAO, GUANGHUA · 2021 to 2024
$1.4M
Integrative Analysis to Identify Regulation Targets of RNA-Binding ProteinsR01GM115473 · NIGMS · UT SOUTHWESTERN MEDICAL CENTER · PI XIE, YANG · 2016 to 2019
$1.3M
Deciphering molecular mechanisms of Epithelial PlasticityK00CA253718 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BAKER, EMILY ARNER · 2022 to 2025
$397k
NCI NIH HHS F99 CA253718NCI NIH HHS K00 CA253718NCI NIH HHS P30 CA142543NCI NIH HHS P50 CA070907NCI NIH HHS R01 CA207763NCI NIH HHS R01 CA273585NCI NIH HHS U01 CA249245NIAID NIH HHS U01 AI156189NIDCR NIH HHS R01 DE030656NIGMS NIH HHS R01 GM115473NIGMS NIH HHS R01 GM140012NIGMS NIH HHS R01 GM141519NIGMS NIH HHS R35 GM136375
6 · The paper itself

Abstract

Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regions, a deep learning model for region recognition was trained based on hematoxylin staining only, achieving pixel accuracy for each class ranging from 0.92 to 0.99. Within the desired area, the algorithm categorizes DAB intensity of each pixel as negative, weak, moderate, or strong staining and calculates the final H-score based on the percentage of each intensity category. Overall, this algorithm takes an IHC image as input and directly outputs the H-score within a few seconds, significantly enhancing the speed of IHC image analysis. This automated tool provides H-score quantification with precision and consistency comparable to experienced pathologists but at a significantly reduced cost during IHC diagnostic workups. It holds significant potential to advance biomedical research reliant on IHC staining for protein expression quantification.

Indexed as

Deep LearningAlgorithmsCell NucleusHematoxylinHumansImmunohistochemistryHematoxylindeep learningH-scoreimmunohistochemistry imagepathology image analysisprotein expression quantification

Identifiers

PMID38043788
PMCPMC11141889
OpenAlexW4389247012

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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.