Evidence map›Paper›PMID 36569159›Full record

ArticleFrontiers in medicine2022

A mucosal recovery software tool for endoscopic submucosal dissection in early gastric cancer.

Yinuo Zhao, Huogen Wang, Yanyan Fan, Chaohui Jin, Qinwei Xu, Jiyong Jing, Tianqiao Zhang, Xuedong Zhang, Wanyuan Chen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2022. 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
0.2field-weighted citation impact, top 46% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  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

9 authors at 6 institutions in 1 country.

Yinuo ZhaoDepartment of Pathology, Rizhao People's Hospital, Rizhao, China.
Huogen WangHithink RoyalFlush Information Network Co., Ltd., Hangzhou, China.
Yanyan FanDepartment of Pathology, Liaocheng People's Hospital, Liaocheng, China.
Chaohui JinHithink RoyalFlush Information Network Co., Ltd., Hangzhou, China.
Qinwei XuEndoscopy Center, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Jiyong JingDepartment of Medical Education & Simulation Center, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.
Tianqiao ZhangHangzhou No.14 High School, Hangzhou, Zhejiang, China.
Xuedong ZhangDepartment of Pathology, Liaocheng People's Hospital, Liaocheng, China.
Wanyuan ChenCancer Center, Department of Pathology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
Hangzhou Medical College · CNLiaocheng People's Hospital · CNChina Internet Network Information Center · CNPeople’s Hospital of Rizhao · CNShanghai East Hospital · CNZhejiang University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Due to the limited diagnostic ability, the low detection rate of early gastric cancer (EGC) is a serious health threat. The establishment of the mapping between endoscopic images and pathological images can rapidly improve the diagnostic ability to detect EGC. To expedite the learning process of EGC diagnosis, a mucosal recovery map for the mapping between ESD mucosa specimen and pathological images should be performed in collaboration with endoscopists and pathologists, which is a time-consuming and laborious work. Methods: 20 patients at the Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College from March 2020 to July 2020 were enrolled in this study. We proposed the improved U-Net to obtain WSI-level segmentation results, and the WSI-level results can be mapped to the macroscopic image of the specimen. For the convenient use, a software pipeline named as "Pathology Helper" for integration the workflow of the construction of mucosal recovery maps was developed. Results: The MIoU and Dice of our model can achieve 0.955 ± 0.0936 and 0.961 ± 0.0874 for WSI-level segmentation, respectively. With the help of "Pathology Helper", we can construct the high-quality mucosal recovery maps to reduce the workload of endoscopists and pathologists. Conclusion: "Pathology Helper" will accelerate the learning of endoscopists and pathologists, and rapidly improve their abilities to detect EGC. Our work can also improve the detection rate of early gastric cancer, so that more patients with gastric cancer will be treated in a timely manner.

Indexed as

artificial intelligenceearly gastric cancerendoscopic submucosal dissectionmucosal recovery mapPathology Helper

Identifiers

PMID36569159
PMCPMC9768361
OpenAlexW4311846951

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.