학술논문
Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients
Document Type
article
Author
Eloise Galzin; Laurent Roche; Anna Vlachomitrou; Olivier Nempont; Heike Carolus; Alexander Schmidt-Richberg; Peng Jin; Pedro Rodrigues; Tobias Klinder; Jean-Christophe Richard; Karim Tazarourte; Marion Douplat; Alain Sigal; Maude Bouscambert-Duchamp; Salim Aymeric Si-Mohamed; Sylvain Gouttard; Adeline Mansuy; François Talbot; Jean-Baptiste Pialat; Olivier Rouvière; Laurent Milot; François Cotton; Philippe Douek; Antoine Duclos; Muriel Rabilloud; Loic Boussel
Source
Research in Diagnostic and Interventional Imaging, Vol 4, Iss , Pp 100018- (2022)
Subject
Language
English
ISSN
2772-6525
Abstract
Objectives: We evaluated the contribution of lung lesion quantification on chest CT using a clinical Artificial Intelligence (AI) software in predicting death and intensive care units (ICU) admission for COVID-19 patients. Methods: For 349 patients with positive COVID-19-PCR test that underwent a chest CT scan at admittance or during hospitalization, we applied the AI for lung and lung lesion segmentation to obtain lesion volume (LV), and LV/Total Lung Volume (TLV) ratio. ROC analysis was used to extract the best CT criterion in predicting death and ICU admission. Two prognostic models using multivariate logistic regressions were constructed to predict each outcome and were compared using AUC values. The first model (“Clinical”) was based on patients’ characteristics and clinical symptoms only. The second model (“Clinical+LV/TLV”) included also the best CT criterion. Results: LV/TLV ratio demonstrated best performance for both outcomes; AUC of 67.8% (95% CI: 59.5 - 76.1) and 81.1% (95% CI: 75.7 - 86.5) respectively. Regarding death prediction, AUC values were 76.2% (95% CI: 69.9 - 82.6) and 79.9% (95%IC: 74.4 - 85.5) for the “Clinical” and the “Clinical+LV/TLV” models respectively, showing significant performance increase (+ 3.7%; p-value