| Title |
A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia
|
|---|---|
| Published in |
European Radiology Experimental, April 2023
|
| DOI | 10.1186/s41747-023-00334-z |
| Pubmed ID | |
| Authors |
Camilla Scapicchio, Andrea Chincarini, Elena Ballante, Luca Berta, Eleonora Bicci, Chandra Bortolotto, Francesca Brero, Raffaella Fiamma Cabini, Giuseppe Cristofalo, Salvatore Claudio Fanni, Maria Evelina Fantacci, Silvia Figini, Massimo Galia, Pietro Gemma, Emanuele Grassedonio, Alessandro Lascialfari, Cristina Lenardi, Alice Lionetti, Francesca Lizzi, Maurizio Marrale, Massimo Midiri, Cosimo Nardi, Piernicola Oliva, Noemi Perillo, Ian Postuma, Lorenzo Preda, Vieri Rastrelli, Francesco Rizzetto, Nicola Spina, Cinzia Talamonti, Alberto Torresin, Angelo Vanzulli, Federica Volpi, Emanuele Neri, Alessandra Retico |
| Abstract |
The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 5 | 38% |
| Lecturer | 1 | 8% |
| Researcher | 1 | 8% |
| Professor > Associate Professor | 1 | 8% |
| Student > Postgraduate | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 4 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 38% |
| Agricultural and Biological Sciences | 1 | 8% |
| Arts and Humanities | 1 | 8% |
| Physics and Astronomy | 1 | 8% |
| Computer Science | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 4 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 April 2023.
All research outputs
#21,089,698
of 23,734,501 outputs
Outputs from European Radiology Experimental
#190
of 216 outputs
Outputs of similar age
#265,468
of 338,608 outputs
Outputs of similar age from European Radiology Experimental
#10
of 10 outputs
Altmetric has tracked 23,734,501 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 216 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 338,608 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one.