Evidence map›Paper›PMID 38473022›Full record

ArticleDiagnostics (Basel, Switzerland)2024

Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters.

Salvatore Claudio Fanni, Federica Volpi, Leonardo Colligiani, Davide Chimera, Michele Tonerini, Francesco Pistelli, Roberta Pancani, Chiara Airoldi, Brian J Bartholmai, Dania Cioni and 5 more

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. 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 3 institutions in 2 countries.

Salvatore Claudio FanniDepartment of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.
Federica VolpiDepartment of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.
Leonardo ColligianiDepartment of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.ORCID 0000-0002-6217-4933
Davide ChimeraPneumology Unit, Pisa University Hospital, 56124 Pisa, Italy.
Michele ToneriniDepartment of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, 56124 Pisa, Italy.
Francesco PistelliPneumology Unit, Pisa University Hospital, 56124 Pisa, Italy.ORCID 0000-0001-5612-6364
Roberta PancaniPneumology Unit, Pisa University Hospital, 56124 Pisa, Italy.ORCID 0000-0001-6835-0937
Chiara AiroldiDepartment of Translational Medicine, University of Eastern Piemonte, 28100 Novara, Italy.ORCID 0000-0002-9114-5816
Brian J BartholmaiDivision of Radiology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0001-7834-6579
Dania CioniDepartment of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.ORCID 0000-0002-5120-886X
Laura CarrozziPneumology Unit, Pisa University Hospital, 56124 Pisa, Italy.
Emanuele NeriDepartment of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.ORCID 0000-0001-7950-4559
Annalisa De Liperi2nd Radiology Unit, Department of Diagnostic Imaging, Pisa University-Hospital, Via Paradisa 2, 56124 Pisa, Italy.
Chiara Romei2nd Radiology Unit, Department of Diagnostic Imaging, Pisa University-Hospital, Via Paradisa 2, 56124 Pisa, Italy.
Pisa COVID-19 Study Group
University of Pisa · ITMayo Clinic · USUniversità degli Studi del Piemonte Orientale “Amedeo Avogadro” · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo quantitatively evaluate CT lung abnormalities in COVID-19 survivors from the acute phase to 24-month follow-up. Quantitative CT features as predictors of abnormalities' persistence were investigated.

methodsPatients who survived COVID-19 were retrospectively enrolled and underwent a chest CT at baseline (T0) and 3 months (T3) after discharge, with pulmonary function tests (PFTs). Patients with residual CT abnormalities repeated the CT at 12 (T12) and 24 (T24) months after discharge. A machine-learning-based software, CALIPER, calculated the CT percentage of the whole lung of normal parenchyma, ground glass (GG), reticulation (Ret), and vascular-related structures (VRSs). Differences (Δ) were calculated between time points. Receiver operating characteristic (ROC) curve analyses were performed to test the baseline parameters as predictors of functional impairment at T3 and of the persistence of CT abnormalities at T12.

resultsThe cohort included 128 patients at T0, 133 at T3, 61 at T12, and 34 at T24. The GG medians were 8.44%, 0.14%, 0.13% and 0.12% at T0, T3, T12 and T24. The Ret medians were 2.79% at T0 and 0.14% at the following time points. All Δ significantly differed from 0, except between T12 and T24. The GG and VRSs at T0 achieved AUCs of 0.73 as predictors of functional impairment, and area under the curves (AUCs) of 0.71 and 0.72 for the persistence of CT abnormalities at T12.

conclusionsCALIPER accurately quantified the CT changes up to the 24-month follow-up. Resolution mostly occurred at T3, and Ret persisting at T12 was almost unchanged at T24. The baseline parameters were good predictors of functional impairment at T3 and of abnormalities' persistence at T12.

Indexed as

COVID-19 pneumoniaCTfollow-uplungmachine learningquantitative computed tomographytexture analysis

Identifiers

PMID38473022
PMCPMC10931356
OpenAlexW4392463261

What Socratic 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 Socratic graph.