Evidence map›Paper›PMID 31803308›Full record

ArticleGastroenterology research2019

Diagnosis of Liver Neoplasms by Computational and Statistical Image Analysis.

Rong Xia, Amir M Boroujeni, Stephanie Shea, Yongsheng Pan, Raag Agrawal, Elhem Yousefi, M Isabel Fiel, M A Haseeb, Raavi Gupta

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

Article in Gastroenterology research, 2019. 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
0.4field-weighted citation impact, top 35% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
  3. Evolution of the liver biopsy and its future.Translational gastroenterology and hepatology · 2021
    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 at 3 institutions in 1 country.

Rong XiaDepartment of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA.
Amir M BoroujeniDepartment of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA.
Stephanie SheaDepartment of Pathology, Mount Sinai Hospital and Icahn School of Medicine, New York, NY 10029, USA.
Yongsheng PanDepartment of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA.
Raag AgrawalDepartment of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA.
Elhem YousefiDepartment of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA.
M Isabel FielDepartment of Pathology, Mount Sinai Hospital and Icahn School of Medicine, New York, NY 10029, USA.
M A HaseebDepartment of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA.
Raavi GuptaDepartment of Pathology, State University of New York, Downstate Medical Center, Brooklyn, NY 11203, USA.
State University of New York · USSUNY Downstate Health Sciences University · USMount Sinai Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDistinguishing well-differentiated hepatocellular carcinoma (WD-HCC), hepatocellular adenoma (HA) and non-neoplastic liver tissue (NNLT) solely on morphology is often challenging. The purpose of this study was to evaluate the use of computational image analysis to distinguish WD-HCC, HA and NNLT.

methodsSeventy-seven cases comprising of WD-HCC (n = 26), HA (n = 23) and NNLT (n = 28) were retrieved and reviewed. A total of 485 hematoxylin and eosin (H&E) photomicrographs (× 400, 0.09 µm

resultsUnsupervised cluster analysis identified three well defined clusters of WD-HCC, HA and NNLT. Employing the four most discriminating nuclear features, supervised analysis was performed on a training set of 383 images, and validated on the remaining 102 test images. The analysis identified WD-HCC (sensitivity 100%, specificity 98%), HA (sensitivity 71%, specificity 85%) and NNLT (sensitivity 70%, specificity 86%). An analysis of whole-slide images identified WD-HCC with sensitivity and specificity of 100%.

conclusionsWe have successfully demonstrated that computational image analysis of nuclear features can differentiate WD-HCC from non-malignant liver with high accuracy, and can be used to assist in the histopathological diagnosis of hepatocellular carcinoma.

Indexed as

Computational analysisHepatocellular adenomaHepatocellular carcinomaImage analysisLiver

Identifiers

PMID31803308
PMCPMC6879028
OpenAlexW2990357513

What OpenQuestion holds

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Registered trials

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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.