Evidence map›Paper›PMID 22053785›Full record

ReviewAmerican journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons2012

Digital transplantation pathology: combining whole slide imaging, multiplex staining and automated image analysis.

K Isse, A Lesniak, K Grama, B Roysam, M I Minervini, A J Demetris

Abstract readReview
In one paragraph

Review in American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
3.2field-weighted citation impact, top 8% 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

33 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025
    Review
  4. Review
  5. Thyroid Cytopathology Cancer Diagnosis from Smartphone Images Using Machine Learning.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2023
    Article
  6. Article
  7. Co-administration ofCurrent topics in medicinal chemistry · 2022
    Article
  8. Review
  9. Article
  10. Review
  11. Commentary: The Digital Fate of Glomeruli in Renal Biopsy.Journal of pathology informatics · 2021
    Article
  12. Banff Digital Pathology Working Group: Going digital in transplant pathology.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2020
    Article
  13. Article
  14. Review
  15. Review
  16. Immune Cell and Cell Cluster Phenotyping, Quantitation, and Visualization Using In Silico Multiplexed Images and Tissue Cytometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019
    Article
  17. Review
  18. Review
  19. Review
  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 at 3 institutions in 1 country.

K IsseDepartment of Pathology, Division of Transplantation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
A Lesniak
K Grama
B Roysam
M I Minervini
A J Demetris
University of Houston · USUniversity of Pittsburgh Medical Center · USUniversity of Pittsburgh · US

Funding

Tolerance Through Induction of Regulatory T CellsP01AI064343 · NIAID · YALE UNIVERSITY · PI ROTHSTEIN, DAVID M · 2006 to 2010
$7.0M
Liver Antigen - Presenting Cells in Hepatic Injury and TransplantationP01AI081678 · NIAID · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI THOMSON, ANGUS W · 2009 to 2010
$2.8M
Multi-dimensional Image Analysis Tools for Brain TissueR01EB005157 · NIBIB · UNIVERSITY OF HOUSTON · PI ROYSAM, BADRINATH · 2008 to 2010
$2.3M
Genotype and phenotype predictors in therapy response in renal cell carcinomaR01CA135509 · NCI · UNIVERSITY OF PENNSYLVANIA · PI FLAHERTY, KEITH T, LEE, WILLIAM M · 2009 to 2013
$2.3M
IMMUNE TOLERANCE NETWORK-266015416-266015416N01AI015416 · NIAID · REGENTS OF THE UNIVERSITY OF C · PI BLUESTONE, JEFFREY A · 2002 to 2006
–
NCI NIH HHS R01 CA135509NCI NIH HHS R01-CA-135509NIAID NIH HHS N01 AI015416NIAID NIH HHS P01 AI064343NIAID NIH HHS P01 AI081678NIAID NIH HHS R01-AI-081678NIBIB NIH HHS R01 EB005157NIBIB NIH HHS R01-EB-005157PHS HHS P01-A1-064343PHS HHS U01-A1-077867
6 · The paper itself

Abstract

Conventional histopathology is the gold standard for allograft monitoring, but its value proposition is increasingly questioned. "-Omics" analysis of tissues, peripheral blood and fluids and targeted serologic studies provide mechanistic insights into allograft injury not currently provided by conventional histology. Microscopic biopsy analysis, however, provides valuable and unique information: (a) spatial-temporal relationships; (b) rare events/cells; (c) complex structural context; and (d) integration into a "systems" model. Nevertheless, except for immunostaining, no transformative advancements have "modernized" routine microscopy in over 100 years. Pathologists now team with hardware and software engineers to exploit remarkable developments in digital imaging, nanoparticle multiplex staining, and computational image analysis software to bridge the traditional histology-global "-omic" analyses gap. Included are side-by-side comparisons, objective biopsy finding quantification, multiplexing, automated image analysis, and electronic data and resource sharing. Current utilization for teaching, quality assurance, conferencing, consultations, research and clinical trials is evolving toward implementation for low-volume, high-complexity clinical services like transplantation pathology. Cost, complexities of implementation, fluid/evolving standards, and unsettled medical/legal and regulatory issues remain as challenges. Regardless, challenges will be overcome and these technologies will enable transplant pathologists to increase information extraction from tissue specimens and contribute to cross-platform biomarker discovery for improved outcomes.

Indexed as

AutomationImage Processing, Computer-AssistedPathologyTransplantationHumansModels, Theoretical

Identifiers

PMID22053785
PMCPMC3627485
OpenAlexW1560884792

What OpenQuestion holds

Textmetadata
LicenceTDM
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.