Evidence map›Paper›PMID 26215864›Full record

ReviewSeminars in nephrology2015

Morphology in the Digital Age: Integrating High-Resolution Description of Structural Alterations With Phenotypes and Genotypes.

Cynthia C Nast, Kevin V Lemley, Jeffrey B Hodgin, Serena Bagnasco, Carmen Avila-Casado, Stephen M Hewitt, Laura Barisoni

Abstract readReview
In one paragraph

Review in Seminars in nephrology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Quantification of Glomerular Structural Lesions: Associations With Clinical Outcomes and Transcriptomic Profiles in Nephrotic Syndrome.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2022
    Observational
  7. Article
  8. Review
  9. Review
  10. Ultrastructural Characterization of Proteinuric Patients Predicts Clinical Outcomes.Journal of the American Society of Nephrology : JASN · 2020
    Observational
  11. Article
  12. Article
  13. CureGN Study Rationale, Design, and Methods: Establishing a Large Prospective Observational Study of Glomerular Disease.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2019
    Observational
  14. Article
  15. Interstitial fibrosis scored on whole-slide digital imaging of kidney biopsies is a predictor of outcome in proteinuric glomerulopathies.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2018
    Article
  16. Review
  17. Article
  18. Article
  19. Reproducibility of the NEPTUNE descriptor-based scoring system on whole-slide images and histologic and ultrastructural digital images.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2016
    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

7 authors.

Cynthia C NastDepartment of Pathology, Cedars-Sinai Medical Center, Los Angeles, CA.
Kevin V LemleyDivision of Nephrology(,) Children's Hospital Los Angeles, Los Angeles, CA.
Jeffrey B HodginDepartment of Pathology, University of Michigan, Ann Arbor, MI.
Serena BagnascoDepartment of Pathology, Johns Hopkins University Medical Center, Baltimore, MD.
Carmen Avila-CasadoDepartment of Pathology, University of Toronto, Toronto, Ontario, Canada.
Stephen M HewittExperimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD.
Laura BarisoniDepartment of Pathology, University of Miami, Miami, FL. Electronic address: LBarisoni@med.miami.edu.

Funding

Training Program in Clinical & Translationa Research in Human Glomerular DiseaseU54DK083912 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KRETZLER, MATTHIAS · 2009 to 2023
$20.3M
Systems Biology of Kidney DiseaseR13DK095680 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KRETZLER, MATTHIAS · 2012 to 2012
$12k
Intramural NIH HHSNIDDK NIH HHS R13 DK095680NIDDK NIH HHS U54 DK083912NIDDK NIH HHS U54-DK-083912
6 · The paper itself

Abstract

Conventional light microscopy has been used to characterize and classify renal diseases, evaluate histopathology in studies and trials, and educate renal pathologists and nephrologists. The advent of digital pathology, in which a glass slide can be scanned to create whole slide images (WSIs) for viewing and manipulating on a computer monitor, provides real and potential advantages compared with conventional light microscopy. Software tools such as annotation, morphometry, and image analysis can be applied to WSIs for studies or educational purposes, and the digital images are available globally to clinicians, pathologists, and investigators. New ways of assessing renal pathology with observational data collection may allow better morphologic correlations and integration with molecular and genetic signatures, refinements of classification schema, and understanding of disease pathogenesis. In multicenter studies, WSIs, which require additional quality assurance steps, provide efficiency by reducing slide shipping and consensus conference costs, and they allow slide viewing anytime and anywhere. Although validation studies for the routine diagnostic use of digital pathology still are needed, this is a powerful tool currently available for translational research, clinical trials, and education in renal pathology.

Indexed as

GenotypeHumansImage Processing, Computer-AssistedKidneyKidney DiseasesMicroscopyPhenotypeDigital pathologyglomerulusNEPTUNErenal pathologyvirtual microscopyvirtual slidewhole slide imaging

Identifiers

PMID26215864
PMCPMC4764351

What OpenQuestion holds

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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.