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Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters.
Fanni, Salvatore Claudio; Volpi, Federica; Colligiani, Leonardo; Chimera, Davide; Tonerini, Michele; Pistelli, Francesco; Pancani, Roberta; Airoldi, Chiara; Bartholmai, Brian J; Cioni, Dania; Carrozzi, Laura; Neri, Emanuele; De Liperi, Annalisa; Romei, Chiara.
Affiliation
  • Fanni SC; Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.
  • Volpi F; Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.
  • Colligiani L; Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.
  • Chimera D; Pneumology Unit, Pisa University Hospital, 56124 Pisa, Italy.
  • Tonerini M; Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, 56124 Pisa, Italy.
  • Pistelli F; Pneumology Unit, Pisa University Hospital, 56124 Pisa, Italy.
  • Pancani R; Pneumology Unit, Pisa University Hospital, 56124 Pisa, Italy.
  • Airoldi C; Department of Translational Medicine, University of Eastern Piemonte, 28100 Novara, Italy.
  • Bartholmai BJ; Division of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
  • Cioni D; Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.
  • Carrozzi L; Pneumology Unit, Pisa University Hospital, 56124 Pisa, Italy.
  • Neri E; Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy.
  • De Liperi A; 2nd Radiology Unit, Department of Diagnostic Imaging, Pisa University-Hospital, Via Paradisa 2, 56124 Pisa, Italy.
  • Romei C; 2nd Radiology Unit, Department of Diagnostic Imaging, Pisa University-Hospital, Via Paradisa 2, 56124 Pisa, Italy.
Diagnostics (Basel) ; 14(5)2024 Mar 05.
Article in En | MEDLINE | ID: mdl-38473022
ABSTRACT

BACKGROUND:

To 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.

METHODS:

Patients 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.

RESULTS:

The 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.

CONCLUSIONS:

CALIPER 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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Diagnostics (Basel) Year: 2024 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Diagnostics (Basel) Year: 2024 Document type: Article Affiliation country:
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