Evidence map›Paper›PMID 38240891›Full record

ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2024

Diagnostic performance of deep-learning-based virtual chromoendoscopy in gastric neoplasms.

Sho Suzuki, Yusuke Monno, Ryo Arai, Masaki Miyaoka, Yosuke Toya, Mitsuru Esaki, Takuya Wada, Waku Hatta, Ayaka Takasu, Shigeaki Nagao and 6 more

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In one paragraph

Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 11% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 12 institutions in 1 country.

Sho SuzukiDepartment of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, 6-1-14, Konodai, Ichikawa-Shi, Chiba, 272-0827, Japan. s.sho.salubriter.mail@gmail.com.ORCID 0000-0003-4831-1409
Yusuke MonnoDepartment of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan.
Ryo AraiDepartment of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan.
Masaki MiyaokaDepartment of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Yosuke ToyaDivision of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, Iwate Medical University, Yahaba, Japan.
Mitsuru EsakiDepartment of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukouka, Japan.
Takuya WadaDepartment of Gastroenterology, Kitasato University School of Medicine, Sagamihara, Japan.
Waku HattaDivision of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai, Japan.
Ayaka TakasuDivision of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan.
Shigeaki NagaoMedical Examination Center, Showa General Hospital, Tokyo, Japan.
Fumiaki IshibashiDepartment of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, 6-1-14, Konodai, Ichikawa-Shi, Chiba, 272-0827, Japan.
Yohei MinatoDepartment of Gastrointestinal Endoscopy, NTT Medical Center Tokyo, Tokyo, Japan.
Kenichi KondaDivision of Gastroenterology, Department of Medicine, Showa University School of Medicine, Tokyo, Japan.
Takahiro DohmenDepartment of Gastroenterology, Yuri Kumiai General Hospital, Yurihonjo, Japan.
Kenji MikiDepartment of Internal Medicine, Tsujinaka Hospital Kashiwanoha, Kashiwa, Japan.
Masatoshi OkutomiDepartment of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan.
Tokyo Institute of Technology · JPInternational University of Health and Welfare · JPFukuoka University · JPIwate Medical University · JPKitasato University · JPKyushu University · JPNihon University · JPNTT Medical Center · JPShowa General Hospital · JPShowa University · JPTaoka Hospital · JPTohoku University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundsCycle-consistent generative adversarial network (CycleGAN) is a deep neural network model that performs image-to-image translations. We generated virtual indigo carmine (IC) chromoendoscopy images of gastric neoplasms using CycleGAN and compared their diagnostic performance with that of white light endoscopy (WLE).

methodsWLE and IC images of 176 patients with gastric neoplasms who underwent endoscopic resection were obtained. We used 1,633 images (911 WLE and 722 IC) of 146 cases in the training dataset to develop virtual IC images using CycleGAN. The remaining 30 WLE images were translated into 30 virtual IC images using the trained CycleGAN and used for validation. The lesion borders were evaluated by 118 endoscopists from 22 institutions using the 60 paired virtual IC and WLE images. The lesion area concordance rate and successful whole-lesion diagnosis were compared.

resultsThe lesion area concordance rate based on the pathological diagnosis in virtual IC was lower than in WLE (44.1% vs. 48.5%, p < 0.01). The successful whole-lesion diagnosis was higher in the virtual IC than in WLE images; however, the difference was insignificant (28.2% vs. 26.4%, p = 0.11). Conversely, subgroup analyses revealed a significantly higher diagnosis in virtual IC than in WLE for depressed morphology (41.9% vs. 36.9%, p = 0.02), differentiated histology (27.6% vs. 24.8%, p = 0.02), smaller lesion size (42.3% vs. 38.3%, p = 0.01), and assessed by expert endoscopists (27.3% vs. 23.6%, p = 0.03).

conclusionsThe diagnostic ability of virtual IC was higher for some lesions, but not completely superior to that of WLE. Adjustments are required to improve the imaging system's performance.

Indexed as

Deep LearningStomach NeoplasmsEndoscopyHumansIndigo CarmineIndigo CarmineChromoendoscopyCycle-consistent generative adversarial networkDeep learningDeep neural networkGastric cancer

Identifiers

PMID38240891
OpenAlexW4391031447

What OpenQuestion holds

Textmetadata
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.