Evidence map›Paper›PMID 37553127›Full record

ReviewJournal of gastric cancer2023

Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry.

Kyung Won Kim, Jimi Huh, Bushra Urooj, Jeongjin Lee, Jinseok Lee, In-Seob Lee, Hyesun Park, Seongwon Na, Yousun Ko

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of gastric cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

8 citing papers in PubMed.

  1. Article
  2. Simple clinical parameters to identify sarcopenia 1 year after gastrectomy for gastric cancer.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026
    Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
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.

Kyung Won Kim *Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-1532-5970
Jimi Huh *Department of Radiology, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0002-8832-6165
Bushra UroojBiomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.ORCID https://orcid.org/0009-0008-5660-167X
Jeongjin LeeSchool of Computer Science and Engineering, Soongsil University, Seoul, Korea.ORCID https://orcid.org/0000-0002-4151-6922
Jinseok LeeDepartment of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, Korea.ORCID https://orcid.org/0000-0002-8580-490X
In-Seob LeeDepartment of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-3099-0140
Hyesun ParkBody Imaging Department of Radiology, Lahey Hospital and Medical Center, Burlington, MA, USA.ORCID https://orcid.org/0000-0003-0707-1875
Seongwon NaBiomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.ORCID https://orcid.org/0009-0004-9742-5351
Yousun KoDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-2181-9555

Funding

Korea Health Industry Development Institute HI18C1216
6 · The paper itself

Abstract

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

Indexed as

Artificial intelligenceDeep learningDiagnostic imagingGastric cancerSarcopenia

Identifiers

PMID37553127
PMCPMC10412978

What Socratic 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 Socratic graph.