Evidence map›Paper›PMID 38940910›Full record

ArticleAbdominal radiology (New York)2024

Ultra-high resolution computed tomography with deep-learning-reconstruction: diagnostic ability in the assessment of gastric cancer and the depth of invasion.

Masaya Tanabe, Masahiro Tanabe, Hideko Onoda, Masatoshi Nakashima, Mayumi Higashi, Yosuke Kawano, Keiko Hideura, Takaaki Ueda, Taiga Kobayashi, Katsuyoshi Ito

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Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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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.

  1. Article
4 · The record

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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

10 authors.

Masaya TanabeDepartment of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
Masahiro TanabeDepartment of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan. m-tanabe@yamaguchi-u.ac.jp.ORCID 0000-0002-0302-4257
Hideko OnodaDepartment of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
Masatoshi NakashimaDepartment of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
Mayumi HigashiDepartment of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
Yosuke KawanoDepartment of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
Keiko HideuraDepartment of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
Takaaki UedaDepartment of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
Taiga KobayashiDepartment of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.
Katsuyoshi ItoDepartment of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, 755-8505, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the image quality of ultra-high-resolution CT (U-HRCT) images reconstructed using an improved deep-learning-reconstruction (DLR) method. Additionally, we assessed the utility of U-HRCT in visualizing gastric wall structure, detecting gastric cancer, and determining the depth of invasion.

methodsForty-six patients with resected gastric cancer who underwent preoperative contrast-enhanced U-HRCT were included. The image quality of U-HRCT reconstructed using three different methods (standard DLR [AiCE], improved DLR-AiCE-Body Sharp [improved AiCE-BS], and hybrid-IR [AIDR3D]) was compared. Visualization of the gastric wall's three-layered structure in four regions and the visibility of gastric cancers were compared between U-HRCT and conventional HRCT (C-HRCT). The diagnostic ability of U-HRCT with the improved AiCE-BS for determining the depth of invasion of gastric cancers was assessed using postoperative pathology specimens.

resultsThe mean noise level of U-HRCT with the improved AiCE-BS was significantly lower than that of the other two methods (p < 0.001). The overall image quality scores of the improved AiCE-BS images were significantly higher (p < 0.001). U-HRCT demonstrated significantly better conspicuity scores for the three-layered structure of the gastric wall than C-HRCT in all regions (p < 0.001). In addition, U-HRCT was found to have superior visibility of gastric cancer in comparison to C-HRCT (p < 0.001). The correct diagnostic rates for determining the depth of invasion of gastric cancer using C-HRCT and U-HRCT were 80%.

conclusionsU-HRCT reconstructed with the improved AiCE-BS provides clearer visualization of the three-layered gastric wall structure than other reconstruction methods. It is also valuable for detecting gastric cancer and assessing the depth of invasion.

Indexed as

Deep LearningNeoplasm InvasivenessRadiographic Image Interpretation, Computer-AssistedStomach NeoplasmsTomography, X-Ray ComputedAdultAgedAged, 80 and overContrast MediaFemaleHumansMaleMiddle AgedRetrospective StudiesContrast MediaGastric cancerImage reconstructionMultidetector computed tomographyNeoplasm invasion

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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.