Evidence map›Paper›PMID 36457374›Full record

ReviewGastroenterology report2022

Artificial intelligence in gastric cancer: applications and challenges.

Runnan Cao, Lei Tang, Mengjie Fang, Lianzhen Zhong, Siwen Wang, Lixin Gong, Jiazheng Li, Di Dong, Jie Tian

Open access · diamondAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Gastroenterology report, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
5.9field-weighted citation impact, top 3% 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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 citations in OpenAlex.

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  18. The Role of Neural Network Analysis in Identifying Predictors of Gastric Cancer.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2024
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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

9 authors at 4 institutions in 1 country.

Runnan CaoSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, P. R. China.
Lei TangKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, P. R. China.
Mengjie FangSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, P. R. China.
Lianzhen ZhongSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, P. R. China.
Siwen WangSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, P. R. China.
Lixin GongCollege of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, Liaoning, P. R. China.
Jiazheng LiKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, P. R. China.
Di DongSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, P. R. China.ORCID https://orcid.org/0000-0003-0783-3171
Jie TianBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, P. R. China.
Chinese Academy of Sciences · CNPeking University · CNNortheastern University · CNXidian University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric cancer (GC) is one of the most common malignant tumors with high mortality. Accurate diagnosis and treatment decisions for GC rely heavily on human experts' careful judgments on medical images. However, the improvement of the accuracy is hindered by imaging conditions, limited experience, objective criteria, and inter-observer discrepancies. Recently, the developments of machine learning, especially deep-learning algorithms, have been facilitating computers to extract more information from data automatically. Researchers are exploring the far-reaching applications of artificial intelligence (AI) in various clinical practices, including GC. Herein, we aim to provide a broad framework to summarize current research on AI in GC. In the screening of GC, AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation. In the diagnosis of GC, AI can support tumor-node-metastasis (TNM) staging and subtype classification. For treatment decisions, AI can help with surgical margin determination and prognosis prediction. Meanwhile, current approaches are challenged by data scarcity and poor interpretability. To tackle these problems, more regulated data, unified processing procedures, and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

Indexed as

artificial intelligencecomputed tomographyendoscopygastric cancerpathologyradiomics

Identifiers

PMID36457374
PMCPMC9707405
OpenAlexW4310641306

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.