Evidence map›Paper›PMID 39529836›Full record

ReviewFrontiers in oncology2024

Applications of artificial intelligence in digital pathology for gastric cancer.

Sheng Chen, Ping'an Ding, Honghai Guo, Lingjiao Meng, Qun Zhao, Cong Li

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

6 authors.

Sheng ChenSchool of Clinical Medicine, Hebei University, Affiliated Hospital of Hebei University, Baoding, China.
Ping'an DingThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang Hebei, China.
Honghai GuoThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang Hebei, China.
Lingjiao MengThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang Hebei, China.
Qun ZhaoThe Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang Hebei, China.
Cong LiSchool of Clinical Medicine, Hebei University, Affiliated Hospital of Hebei University, Baoding, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastric cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in worldwide. Early diagnosis and treatment are essential for a positive outcome. The integration of artificial intelligence in the pathology field is increasingly widespread, including histopathological images analysis. In recent years, the application of digital pathology technology emerged as a potential solution to enhance the understanding and management of gastric cancer. Through sophisticated image analysis algorithms, artificial intelligence technologies facilitate the accuracy and sensitivity of gastric cancer diagnosis and treatment and personalized therapeutic strategies. This review aims to evaluate the current landscape and future potential of artificial intelligence in transforming gastric cancer pathology, so as to provide ideas for future research.

Indexed as

artificial intelligencecomputational pathologydigital pathologygastric cancermachine learning

Identifiers

PMID39529836
PMCPMC11551048

What OpenQuestion holds

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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.