Evidence map›Paper›PMID 36304779›Full record

ArticleContrast media & molecular imaging2022

Construction of Stomach Cancer Lesion Detection Combined with Drug Therapy Based on Artificial Intelligence.

Shengyong Zhai, Lihong Yu, Jing Li

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 40% 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, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Shengyong ZhaiDepartment of General Surgery, Weifang People's Hospital, Weifang 261041, Shandong, China.
Lihong YuDepartment of Clinical Pharmacy, Weifang People's Hospital, Weifang 261041, Shandong, China.
Jing LiPharmaceutical Clinical Research Center, Weifang People's Hospital, Weifang 261041, Shandong, China.ORCID 0000-0001-6987-1515
Weifang People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The number of stomach cancer (SC) patients is increasing sharply every year, and gastroscope is a common method to check stomach-related diseases. A bulging lesion in the stomach is encountered during a gastroscopy. Due to the change in eating habits, the enhancement of health awareness, and the wide application of gastroscopy, the detection rate and cure rate of tumors have been significantly improved. This has certain clinical value for the early diagnosis and treatment of early SC. In this paper, based on the background of artificial intelligence, image segmentation technology is used to analyze and process the detection results of SC, so as to judge the effect of drug treatment. A total of 1408 gastric bulge lesions were investigated in 11023 patients during the one-year period 2019-2020. It also analyzed the age, lesion location, size, pathological type, and tumor detection results of 1408 patients. The experiment showed that among the 289 cases of submucosal bulging lesions, the detection rates of the young group, middle-aged group, and elderly group were 14.9% (43/289), 67.5% (195/289), and 17.6% (51/289), respectively. Among them, middle-aged people aged 41-65 have the highest detection rate. The incidence of gastric polyps was similar between different age groups. But with age, the rate of fundic gland polyps increases. The incidence of SC is not related to the age of the patient, but to its pathological type. The incidence of SC in middle-aged and elderly people is significantly higher than that in young people. SC is more common in the cardia, and gastrointestinal stromal tumors are most common with submucosal protrusion.

Indexed as

Stomach NeoplasmsAdenomatous PolypsAdolescentAdultAgedArtificial IntelligenceGastroscopyHumansMiddle Aged

Identifiers

PMID36304779
PMCPMC9578819
OpenAlexW4304588655

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.