Evidence map›Paper›PMID 33304203›Full record

ReviewContrast media & molecular imaging2020

Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging.

Cai-Wei Yang, Xi-Jiao Liu, Si-Yun Liu, Shang Wan, Zheng Ye, Bin Song

Open access · hybridAbstract readReview
In one paragraph

Review in Contrast media & molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Review
  3. 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

6 authors at 1 institution in 1 country.

Cai-Wei Yang *Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.ORCID 0000-0003-3335-3948
Xi-Jiao Liu *Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.ORCID 0000-0002-6900-0696
Si-Yun LiuGE Healthcare (China), Beijing 100176, China.ORCID 0000-0001-5611-382X
Shang WanDepartment of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.ORCID 0000-0001-9182-1709
Zheng YeDepartment of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.ORCID 0000-0001-6715-0183
Bin SongDepartment of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.ORCID 0000-0002-7269-2101
Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The most common mesenchymal tumors are gastrointestinal stromal tumors (GISTs), which have malignant potential and can occur anywhere along the gastrointestinal system. Imaging methods are important and indispensable of GISTs in diagnosis, risk staging, therapy, and follow-up. The recommended imaging method for staging and follow-up is computed tomography (CT) according to current guidelines. Artificial intelligence (AI) applies and elaborates theses, procedures, modes, and utilization systems for simulating, enlarging, and stretching the intellectual capacity of humans. Recently, researchers have done a few studies to explore AI applications in GIST imaging. This article reviews the present AI studies in GISTs imaging, including preoperative diagnosis, risk stratification and prediction of prognosis, gene mutation, and targeted therapy response.

Indexed as

Artificial IntelligenceAnimalsGastrointestinal NeoplasmsGastrointestinal Stromal TumorsHumansImage Processing, Computer-AssistedTomography, X-Ray Computed

Identifiers

PMID33304203
PMCPMC7714601
OpenAlexW3109944679

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.