| Autor: |
Axel Bartoli, MD, Joris Fournel, Arnaud Maurin, MD, Baptiste Marchi, MD, Paul Habert, MD, Maxime Castelli, MD, Jean-Yves Gaubert, MD, Sebastien Cortaredona, MD, Jean-Christophe Lagier, MD, PhD, Matthieu Million, MD, PhD, Didier Raoult, MD, PhD, Badih Ghattas, MCU, Alexis Jacquier, MD, PhD |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Research in Diagnostic and Interventional Imaging, Vol 1, Iss , Pp 100003- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2772-6525 |
| DOI: |
10.1016/j.redii.2022.100003 |
| Popis: |
Objectives: 1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification. Methods: This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization>10 days, intensive care unit hospitalization or oxygen therapy. Results: The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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