Evidence map›Paper›PMID 33496812›Full record

ReviewDer Pathologe2021

[Radiological features of interstitial lung diseases].

Sabine Dettmer, Sarah Scharm, Hoen-Oh Shin

Abstract readReview
PubMed Publisher
In one paragraph

Review in Der Pathologe, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact, top 99% 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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025
    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

3 authors at 1 institution in 1 country.

Sabine DettmerInstitut für Diagnostische und Interventionelle Radiologie, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland. dettmer.sabine@mh-hannover.de.
Sarah ScharmInstitut für Diagnostische und Interventionelle Radiologie, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.
Hoen-Oh ShinInstitut für Diagnostische und Interventionelle Radiologie, Medizinische Hochschule Hannover, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.
Medizinische Hochschule Hannover · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In addition to pneumology and pathology, radiology is an essential discipline in the interdisciplinary diagnosis of interstitial lung diseases (ILDs). The gold standard for diagnosis of ILD is computed tomography. Diagnostic findings are based on specific radiological signs such as interlobular septal thickening and nodular changes. From these signs and their distribution within the lung, radiological patterns can be derived, e.g., usual interstitial pneumonia, nonspecific interstitial pneumonia, or organizing pneumonia. Various differential diagnoses result from the radiological pattern, which can then be further limited in an interdisciplinary manner with the clinic and pathology and, if necessary, trigger further diagnostics.The visual assessment of interstitial lung changes requires experience and training and is nevertheless error-prone with high inter- and intraobserver variabilities. Recently, therefore, computer-aided analysis of ILDs has been increasingly promoted. These computer programs analyze the density distribution of the lung parenchyma using parameters such as mean lung density, skewness, and kurtosis thus enabling the quantification and assessment of the course of disease. Furthermore, texture analysis and artificial intelligence are used to characterize parenchymal changes and differentiate between regions of ground glass, reticulation, and honeycombing. Modern dual-energy CT methods allow a combined, regional recording of both the morphology and the function and provide information about regional ventilation and perfusion.

Indexed as

Lung Diseases, InterstitialRadiologyArtificial IntelligenceHumansLungTomography, X-Ray ComputedArtificial intelligenceComputed tomographyComputer-aided analysisPulmonary fibrosisRadiology

Identifiers

PMID33496812
OpenAlexW3125864065

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.