Evidence map›Paper›PMID 29467917›Full record

ArticleWorld journal of gastrointestinal oncology2018

Preliminary study of automatic gastric cancer risk classification from photofluorography.

Ren Togo, Kenta Ishihara, Katsuhiro Mabe, Harufumi Oizumi, Takahiro Ogawa, Mototsugu Kato, Naoya Sakamoto, Shigemi Nakajima, Masahiro Asaka, Miki Haseyama

Abstract read
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In one paragraph

Article in World journal of gastrointestinal oncology, 2018. 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
–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

3 citing papers in PubMed.

  1. Observational
  2. Article
  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

10 authors.

Ren TogoGraduate School of Information Science and Technology, Hokkaido University, Hokkaido 060-0814, Japan.
Kenta IshiharaGraduate School of Information Science and Technology, Hokkaido University, Hokkaido 060-0814, Japan.
Katsuhiro MabeDepartment of Gastroenterology, National Hospital Organization Hakodate Hospital, Hokkaido 041-8512, Japan.
Harufumi OizumiMedical Examination Center of the Yamagata City Medical Association, Yamagata 990-2473, Japan.
Takahiro OgawaGraduate School of Information Science and Technology, Hokkaido University, Hokkaido 060-0814, Japan.
Mototsugu KatoDepartment of Gastroenterology, National Hospital Organization Hakodate Hospital, Hokkaido 041-8512, Japan.
Naoya SakamotoDepartment of Gastroenterology, Hokkaido University Graduate School of Medicine, Hokkaido 060-8648, Japan.
Shigemi NakajimaDepartment of General Medicine, Japan Community Healthcare Organization Shiga Hospital, Shiga 520-0846, Japan.
Masahiro AsakaHealth Sciences University of Hokkaido, Hokkaido 061-0293, Japan.
Miki HaseyamaGraduate School of Information Science and Technology, Hokkaido University, Hokkaido 060-0814, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo perform automatic gastric cancer risk classification using photofluorography for realizing effective mass screening as a preliminary study.

methodsWe used data for 2100 subjects including X-ray images, pepsinogen I and II levels, PGI/PGII ratio,

resultsSensitivity, specificity and Youden index (YI) of

conclusionAlthough further improvements of the system are needed, experimental results indicated the effectiveness of machine learning techniques for estimation of gastric cancer risk.

Indexed as

Automatic data processingGastric cancerHelicobacter pyloriMass screeningPhotofluorography

Identifiers

PMID29467917
PMCPMC5807881

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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.