Evidence map›Paper›PMID 32012497›Full record

ArticleCancer medicine2020

Computational analysis of morphological and molecular features in gastric cancer tissues.

Yoko Yasuda, Kazuaki Tokunaga, Tomoaki Koga, Chiyomi Sakamoto, Ilya G Goldberg, Noriko Saitoh, Mitsuyoshi Nakao

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 18% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Material Safety of Styrene-Block-Ethylene/Butylene-Block-Styrene Copolymers Used for Cardiac Valves: 6-Month In Vivo Results from a Juvenile Sheep Model.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2025
    Article
  3. Article
  4. Article
  5. Review
  6. Molecular Chaperones and Thyroid Cancer.International journal of molecular sciences · 2021
    Review
  7. Review
  8. Article
  9. Artificial Intelligence in Medicine: A Specialty-Level Overview of Emerging AI Trends.JSLS : Journal of the Society of Laparoendoscopic Surgeons
    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

7 authors at 4 institutions in 2 countries.

Yoko YasudaDepartment of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan.
Kazuaki TokunagaDepartment of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan.
Tomoaki KogaDepartment of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan.
Chiyomi SakamotoDepartment of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan.
Ilya G GoldbergImage Informatics and Computational Biology Unit, Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Noriko SaitohThe Cancer Institute of JFCR, Tokyo, Japan.
Mitsuyoshi NakaoDepartment of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan.ORCID 0000-0002-2196-8673
Kumamoto University · JPKyushu University · JPNational Institutes of Health · USThe Cancer Institute Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies. In this study, we used a supervised machine learning algorithm wndchrm to classify hematoxylin and eosin (H&E)-stained images of human gastric cancer tissues. This analysis distinguished between noncancer and cancer tissues with different histological grades. We then classified the H&E-stained images by expression levels of cancer-associated nuclear ATF7IP/MCAF1 and membranous PD-L1 proteins using immunohistochemistry of serial sections. Interestingly, classes with low and high expressions of each protein exhibited significant morphological dissimilarity in H&E images. These results indicated that morphological features in cancer tissues are correlated with expression of specific cancer-associated proteins, suggesting the usefulness of biomolecular-based morphological classification.

Indexed as

Machine LearningB7-H1 AntigenBiomarkers, TumorCell MembraneFeasibility StudiesHumansImage Processing, Computer-AssistedImmunohistochemistryRepressor ProteinsStomachStomach NeoplasmsTissue Array AnalysisATF7IP protein, humanB7-H1 AntigenBiomarkers, TumorCD274 protein, humanRepressor ProteinsATF7IP/MCAF1gastric cancermachine learningPD-L1tissue morphology

Identifiers

PMID32012497
PMCPMC7064096
OpenAlexW3005119799

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.