Evidence map›Paper›PMID 32815517›Full record

ArticleeLife2020

3D virtual pathohistology of lung tissue from Covid-19 patients based on phase contrast X-ray tomography.

Marina Eckermann, Jasper Frohn, Marius Reichardt, Markus Osterhoff, Michael Sprung, Fabian Westermeier, Alexandar Tzankov, Christopher Werlein, Mark Kühnel, Danny Jonigk and 1 more

Abstract readVideo-Audio Media
In one paragraph

Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

37 citing papers in PubMed.

  1. Article
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  6. Imaging technologies in experimental pulmonary fibrosis research: essential tool for enhanced translational relevance.European respiratory review : an official journal of the European Respiratory Society · 2025
    Review
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Three-dimensional analysis of human pancreatic cancer specimens by phase-contrast based X-ray tomography - the next dimension of diagnosis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023
    Article
  16. Deep Survival Analysis With Clinical Variables for COVID-19.IEEE journal of translational engineering in health and medicine · 2023
    Article
  17. Article
  18. Article
  19. Article
  20. 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

11 authors.

Marina Eckermann *Institut für Röntgenphysik, Georg-August-Universität, Göttingen, Germany.
Jasper Frohn *Institut für Röntgenphysik, Georg-August-Universität, Göttingen, Germany.
Marius Reichardt *Institut für Röntgenphysik, Georg-August-Universität, Göttingen, Germany.
Markus OsterhoffInstitut für Röntgenphysik, Georg-August-Universität, Göttingen, Germany.
Michael SprungDeutsches Elektronen-Synchrotron (DESY), Hamburg, Germany.
Fabian WestermeierDeutsches Elektronen-Synchrotron (DESY), Hamburg, Germany.
Alexandar TzankovInstitut für Medizinische Genetik und Pathologie, Universitätsspital Basel, Basel, Switzerland.
Christopher WerleinMedizinische Hochschule Hannover (MHH), Hannover, Germany.
Mark KühnelMedizinische Hochschule Hannover (MHH), Hannover, Germany.
Danny JonigkMedizinische Hochschule Hannover (MHH), Hannover, Germany.
Tim SaldittInstitut für Röntgenphysik, Georg-August-Universität, Göttingen, Germany.ORCID 0000-0003-4636-0813

Funding

Botnar Research Center for Child Health BRCCHBotnar Research Center of Child Health BRCCHBundesministerium für Bildung und Forschung 05K19MG2Deutsche Forschungsgemeinschaft -EXC 2067/1-390729940Deutsche Forschungsgemeinschaft EXC 2067/1-390729940H2020 European Research Council 771883Max-Planck School Matter to LifeMax-Planck Schools Matter to Life
6 · The paper itself

Abstract

We present a three-dimensional (3D) approach for virtual histology and histopathology based on multi-scale phase contrast x-ray tomography, and use this to investigate the parenchymal architecture of unstained lung tissue from patients who succumbed to Covid-19. Based on this first proof-of-concept study, we propose multi-scale phase contrast x-ray tomography as a tool to unravel the pathophysiology of Covid-19, extending conventional histology by a third dimension and allowing for full quantification of tissue remodeling. By combining parallel and cone beam geometry, autopsy samples with a maximum cross section of 8 mm are scanned and reconstructed at a resolution and image quality, which allows for the segmentation of individual cells. Using the zoom capability of the cone beam geometry, regions-of-interest are reconstructed with a minimum voxel size of 167 nm. We exemplify the capability of this approach by 3D visualization of diffuse alveolar damage (DAD) with its prominent hyaline membrane formation, by mapping the 3D distribution and density of lymphocytes infiltrating the tissue, and by providing histograms of characteristic distances from tissue interior to the closest air compartment.

Indexed as

Clinical Laboratory TechniquesImaging, Three-DimensionalTomography, X-Ray ComputedAdultAgedAged, 80 and overAutopsyBetacoronavirusBiopsyCoronavirus InfectionsCOVID-19COVID-19 TestingFemaleHost Microbial InteractionsHumansLung3d histologyCovid-19humanimmunologyinflammationlung tissuemedicinephase contrasttomographyx-ray

Identifiers

PMID32815517
PMCPMC7473770

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.