Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients - 02/12/22
, Sylvain Gouttard a, Adeline Mansuy a, François Talbot ab, Jean-Baptiste Pialat a, g, Olivier Rouvière a, ac, Laurent Milot a, ac, François Cotton a, g, Philippe Douek a, g, Antoine Duclos h, Muriel Rabilloud b, x, y, Loic Boussel a, g
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Highlights |
• | Clinically available AI software allows automatic quantification of lung involvement on chest CT scans for COVID-19 patients. |
• | CT findings combined with clinical variables allow better intensive care unit admission and death prediction. |
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<0.001) when adding LV/TLV ratio. Similarly, for ICU admission prediction, AUC values were 74.9% (IC 95%: 69.2 - 80.6) and 84.8% (IC 95%: 80.4 - 89.2) respectively corresponding to significant performance increase (+ 10%: p-value<0.001).
Conclusions |
Using a clinical AI software to quantify the COVID-19 lung involvement on chest CT, combined with clinical variables, allows better prediction of death and ICU admission.
Le texte complet de cet article est disponible en PDF.Keywords : Prognosis, AI, COVID-19, Chest-CT, ICU
Abbreviations : AI, CI, COVID-19, ICU, ENT, PACS, PCR, TLV, LV
Plan
Vol 4
Article 100018- décembre 2022 Retour au numéro
Article précédent
- Prediction model for patient prognosis in idiopathic pulmonary fibrosis using hybrid radiomics analysis
- Daisuke Kawahara, Takeshi Masuda, Riku Nishioka, Masashi Namba, Nobuki Imano, Kakuhiro Yamaguchi, Shinjiro Sakamoto, Yasushi Horimasu, Shintaro Miyamoto, Taku Nakashima, Hiroshi Iwamoto, Shinichiro Ohshimo, Kazunori Fujitaka, Hironobu Hamada, Noboru Hattori, Yasushi Nagata
- Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients
- 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
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