Evidence map›Paper›PMID 33824924›Full record

ReviewTranslational gastroenterology and hepatology2021

Evolution of the liver biopsy and its future.

Dhanpat Jain, Richard Torres, Romulo Celli, Jeremy Koelmel, Georgia Charkoftaki, Vasilis Vasiliou

Abstract readReview
In one paragraph

Review in Translational gastroenterology and hepatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
  20. 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

6 authors.

Dhanpat JainDepartment of Anatomic Pathology, Yale University School of Medicine, New Haven, CT, USA.
Richard TorresDepartment of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA.
Romulo CelliDepartment of Anatomic Pathology, Yale University School of Medicine, New Haven, CT, USA.
Jeremy KoelmelDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.
Georgia CharkoftakiDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.
Vasilis VasiliouDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Yale Liver CenterP30DK034989 · NIDDK · YALE UNIVERSITY · PI YASUKO IWAKIRI · 1986 to 2026
$31.1M
Practical high resolution microscopy of un-cut, un-embedded lung biopsiesR44CA189522 · NCI · APPLIKATE TECHNOLOGIES, INC. · PI LEVENE, MICHAEL JOHN · 2017 to 2025
$5.7M
Mouse Models for Alcohol Metabolism and Tissue InjuryR24AA022057 · NIAAA · YALE UNIVERSITY · PI VASILIOU, VASILIS · 2013 to 2022
$4.5M
Identification of Biomarkers and Novel Pathways of Alcoholic Liver Disease by Leveraging Metabolomics, Tissue Imaging Mass Spectrometry, and Integrative Machine LearningR21AA028432 · NIAAA · YALE UNIVERSITY · PI VASILIOU, VASILIS · 2020 to 2021
$680k
Role of Mitochondrial Aldehyde Dehydrogenases in Ethanol Metabolism and ToxicityR21AA017754 · NIAAA · UNIVERSITY OF COLORADO DENVER · PI VASILIOU, VASILIS · 2009 to 2010
$411k
NCATS NIH HHS UL1 TR001863NCI NIH HHS R44 CA189522NIAAA NIH HHS R21 AA017754NIAAA NIH HHS R21 AA028432NIAAA NIH HHS R24 AA022057NIDDK NIH HHS P30 DK034989
6 · The paper itself

Abstract

Liver biopsies are commonly used to evaluate a wide variety of medical disorders, including neoplasms and post-transplant complications. However, its use is being impacted by improved clinical diagnosis of disorders, and non-invasive methods for evaluating liver tissue and as a result the indications of a liver biopsy have undergone major changes in the last decade. The evolution of highly effective treatments for some of the common indications for liver biopsy in the last decade (e.g., viral hepatitis B and C) has led to a decline in the number of liver biopsies in recent years. At the same time, the emergence of better technologies for histologic evaluation, tissue content analysis and genomics are among the many new and exciting developments in the field that hold great promise for the future and are going to shape the indications for a liver biopsy in the future. Recent advances in slide scanners now allow creation of "digital/virtual" slides that have image of the entire tissue section present in a slide [whole slide imaging (WSI)]. WSI can now be done very rapidly and at very high resolution, allowing its use in routine clinical practice. In addition, a variety of technologies have been developed in recent years that use different light sources and/or microscopes allowing visualization of tissues in a completely different way. One such technique that is applicable to liver specimens combines multiphoton microscopy (MPM) with advanced clearing and fluorescent stains known as Clearing Histology with MultiPhoton Microscopy (CHiMP). Although it has not yet been extensively validated, the technique has the potential to decrease inefficiency, reduce artifacts, and increase data while being readily integrable into clinical workflows. Another technology that can provide rapid and in-depth characterization of thousands of molecules in a tissue sample, including liver tissues, is matrix assisted laser desorption/ionization (MALDI) mass spectrometry. MALDI has already been applied in a clinical research setting with promising diagnostic and prognostic capabilities, as well as being able to elucidate mechanisms of liver diseases that may be targeted for the development of new therapies. The logical next step in huge data sets obtained from such advanced analysis of liver tissues is the application of machine learning (ML) algorithms and application of artificial intelligence (AI), for automated generation of diagnoses and prognoses. This review discusses the evolving role of liver biopsies in clinical practice over the decades, and describes newer technologies that are likely to have a significant impact on how they will be used in the future.

Indexed as

Clearing Histology with MultiPhoton Microscopy (CHiMP)Liver biopsymatrix assisted laser desorption imaging (MALDI)whole slide imaging (WSI)

Identifiers

PMID33824924
PMCPMC7829074

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.