Evidence map›Paper›PMID 35706692›Full record

ArticleFrontiers in neuroscience2022

A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer.

Zixin Han, Junlin Lan, Tao Wang, Ziwei Hu, Yuxiu Huang, Yanglin Deng, Hejun Zhang, Jianchao Wang, Musheng Chen, Haiyan Jiang and 5 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.8field-weighted citation impact, top 9% 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

14 citing papers in PubMed, 20 citations in OpenAlex.

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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

15 authors at 4 institutions in 2 countries.

Zixin HanCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Junlin LanCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Tao WangCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Ziwei HuCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Yuxiu HuangCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Yanglin DengCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Hejun ZhangDepartment of Pathology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, China.
Jianchao WangDepartment of Pathology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, China.
Musheng ChenDepartment of Pathology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, China.
Haiyan JiangFujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, China.
Ren-Guey LeeDepartment of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan.
Qinquan GaoCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Ming DuCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Tong TongCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
Gang ChenCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China.
Fuzhou University · CNFujian Medical University · CNFujian Provincial Cancer Hospital · CNNational Taipei University of Technology · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric cancer is the third most common cause of cancer-related death in the world. Human epidermal growth factor receptor 2 (HER2) positive is an important subtype of gastric cancer, which can provide significant diagnostic information for gastric cancer pathologists. However, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 immunohistochemical WSIs one by one under the microscope. It is a repetitive, tedious, and highly subjective process. Additionally, WSIs have billions of pixels in an image, which poses computational challenges to Computer-Aided Diagnosis (CAD) systems. This study proposed a deep learning algorithm for HER2 quantification evaluation of gastric cancer. Different from other studies that use convolutional neural networks for extracting feature maps or pre-processing on WSIs, we proposed a novel automatic HER2 scoring framework in this study. In order to accelerate the computational process, we proposed to use the re-parameterization scheme to separate the training model from the deployment model, which significantly speedup the inference process. To the best of our knowledge, this is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis. Experiment results have demonstrated the effectiveness of our proposed method with an accuracy of 0.94 for the HER2 scoring prediction.

Indexed as

CNNdeep learninggastric cancerHER2 score predictionre-parameterization

Identifiers

PMID35706692
PMCPMC9190202
OpenAlexW4281930279

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.