Evidence map›Paper›PMID 32429182›Full record

ArticleDiagnostics (Basel, Switzerland)2020

Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT.

Alessandro Stefano, Mauro Gioè, Giorgio Russo, Stefano Palmucci, Sebastiano Emanuele Torrisi, Samuel Bignardi, Antonio Basile, Albert Comelli, Viviana Benfante, Gianluca Sambataro and 5 more

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 4% 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

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025
    Review
  5. Article
  6. Review
  7. Quantitative parameters of HRCT target scan to predict the risk of lung adenocarcinoma based on the detection of lung ground-glass nodules.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
    Article
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Radiomics in Cardiac Computed Tomography.Diagnostics (Basel, Switzerland) · 2023
    Review
  18. COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet.International journal of imaging systems and technology · 2023
    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

15 authors at 5 institutions in 2 countries.

Alessandro StefanoInstitute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy.
Mauro GioèDepartment of Economics, Business, and Statistics (DSEAS), University of Palermo, 90133 Palermo, Italy.
Giorgio RussoInstitute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy.
Stefano PalmucciDepartment of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital "Policlinico-Vittorio Emanuele", 95123 Catania, Italy.
Sebastiano Emanuele TorrisiRegional Referral Centre for Rare Lung Diseases, A.O.U. Policlinico-Vittorio Emanuele, University of Catania, 95123 Catania, Italy.
Samuel BignardiLaboratory of Computational Computer Vision (LCCV), School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Antonio BasileDepartment of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital "Policlinico-Vittorio Emanuele", 95123 Catania, Italy.
Albert ComelliInstitute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy.
Viviana BenfanteInstitute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy.
Gianluca SambataroDepartment of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital "Policlinico-Vittorio Emanuele", 95123 Catania, Italy.
Daniele FalsaperlaDepartment of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital "Policlinico-Vittorio Emanuele", 95123 Catania, Italy.
Alfredo Gaetano TorcittoDepartment of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital "Policlinico-Vittorio Emanuele", 95123 Catania, Italy.
Massimo AttanasioDepartment of Economics, Business, and Statistics (DSEAS), University of Palermo, 90133 Palermo, Italy.
Anthony YezziLaboratory of Computational Computer Vision (LCCV), School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Carlo VancheriRegional Referral Centre for Rare Lung Diseases, A.O.U. Policlinico-Vittorio Emanuele, University of Catania, 95123 Catania, Italy.
Azienda Ospedaliero-Universitaria Policlinico - Vittorio Emanuele · ITInstitute of Molecular Bioimaging and Physiology · ITGeorgia Institute of Technology · USUniversity of Catania · ITUniversity of Palermo · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOur study assesses the diagnostic value of different features extracted from high resolution computed tomography (HRCT) images of patients with idiopathic pulmonary fibrosis. These features are investigated over a range of HRCT lung volume measurements (in Hounsfield Units) for which no prior study has yet been published. In particular, we provide a comparison of their diagnostic value at different Hounsfield Unit (HU) thresholds, including corresponding pulmonary functional tests.

methodsWe consider thirty-two patients retrospectively for whom both HRCT examinations and spirometry tests were available. First, we analyse the HRCT histogram to extract quantitative lung fibrosis features. Next, we evaluate the relationship between pulmonary function and the HRCT features at selected HU thresholds, namely -200 HU, 0 HU, and +200 HU. We model the relationship using a Poisson approximation to identify the measure with the highest log-likelihood.

resultsOur Poisson models reveal no difference at the -200 and 0 HU thresholds. However, inferential conclusions change at the +200 HU threshold. Among the HRCT features considered, the percentage of normally attenuated lung at -200 HU shows the most significant diagnostic utility.

conclusionsThe percentage of normally attenuated lung can be used together with qualitative HRCT assessment and pulmonary function tests to enhance the idiopathic pulmonary fibrosis (IPF) diagnostic process.

Indexed as

High resolution computed tomographyIdiopathic pulmonary fibrosisRadiomics

Identifiers

PMID32429182
PMCPMC7277964
OpenAlexW3025467674

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.