AI-based multi-modal integration (ScanCov scores) of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients
Nathalie Lassau
(1, 2)
,
Samy Ammari
(1, 2)
,
Emilie Chouzenoux
(3)
,
Hugo Gortais
(4)
,
Paul Herent
(5)
,
Matthieu Devilder
(4)
,
Samer Soliman
(4)
,
Olivier Meyrignac
(4)
,
Marie-Pauline Talabard
(4)
,
Jean-Philippe Lamarque
(1, 2)
,
Rémy Dubois
(5)
,
Nicolas Loiseau
(5)
,
Paul Trichelair
(5)
,
Etienne Bendjebbar
(5)
,
Gabriel C Garcia
(2)
,
Corinne Balleyguier
(1, 2)
,
Mansouria Merad
(6)
,
Annabelle Stoclin
(6, 7)
,
Simon Jegou
(5)
,
Franck Griscelli
(6)
,
Nicolas Tetelboum
(2)
,
Yingping Li
(1)
,
Sagar Verma
(3)
,
Matthieu Terris
(3)
,
Tasnim Dardouri
(3)
,
Kavya Gupta
(3)
,
Ana Neacsu
(3)
,
Frank Chemouni
(6)
,
Meriem Sefta
(5)
,
Paul Jehanno
(5)
,
Imad Bousaid
(6)
,
Yannick Boursin
(6)
,
Emmanuel Planchet
(6)
,
Mikael Azoulay
(6)
,
Jocelyn Dachary
(5)
,
Fabien Brulport
(5)
,
Adrián González
(5)
,
Olivier Dehaene
(5)
,
Jean-Baptiste Schiratti
(5)
,
Kathryn Schutte
(5)
,
Jean-Christophe Pesquet
(3)
,
Hugues Talbot
(3)
,
Elodie Pronier
(5)
,
Gilles Wainrib
(5)
,
Thomas Clozel
(5)
,
Fabrice Barlesi
(6)
,
Marie-France Bellin
(1, 4)
,
Michael G B Blum
(5)
1
BIOMAPS -
LaBoratoire d'Imagerie biOmédicale MultimodAle Paris-Saclay
2 Département d'imagerie médicale [Gustave Roussy]
3 OPIS - OPtimisation Imagerie et Santé
4 Hôpital Bicêtre [AP-HP, Le Kremlin-Bicêtre]
5 Owkin France
6 IGR - Institut Gustave Roussy
7 DSA - Département de soins aigus [Gustave Roussy]
2 Département d'imagerie médicale [Gustave Roussy]
3 OPIS - OPtimisation Imagerie et Santé
4 Hôpital Bicêtre [AP-HP, Le Kremlin-Bicêtre]
5 Owkin France
6 IGR - Institut Gustave Roussy
7 DSA - Département de soins aigus [Gustave Roussy]
Nathalie Lassau
- Fonction : Auteur
- PersonId : 758207
- ORCID : 0000-0001-8068-6513
Emilie Chouzenoux
- Fonction : Auteur
- PersonId : 10209
- IdHAL : emilie-chouzenoux
- ORCID : 0000-0003-3631-6093
- IdRef : 192528572
Corinne Balleyguier
- Fonction : Auteur
- PersonId : 1241668
- ORCID : 0000-0002-0018-8731
- IdRef : 074537695
Yingping Li
- Fonction : Auteur
- PersonId : 743094
- IdHAL : yingping-li
Jean-Baptiste Schiratti
- Fonction : Auteur
- PersonId : 6658
- IdHAL : jean-baptiste-schiratti
- ORCID : 0000-0002-8797-1146
- IdRef : 197625320
Jean-Christophe Pesquet
- Fonction : Auteur
- PersonId : 8124
- IdHAL : jean-christophe-pesquet
- ORCID : 0000-0002-5943-8061
- IdRef : 122058577
Hugues Talbot
- Fonction : Auteur
- PersonId : 1939
- IdHAL : hugues-talbot
- ORCID : 0000-0002-2179-3498
- IdRef : 181843056
Fabrice Barlesi
- Fonction : Auteur
- PersonId : 1357222
- ORCID : 0000-0001-5793-3539
- IdRef : 087602997
Résumé
The SARS-COV-2 pandemic has put pressure on Intensive Care Units, and made theidentification of early predictors of disease severity a priority. We collected clinical,biological, chest CT scan data, and radiology reports from 1,003 coronavirus-infectedpatients from two French hospitals. Among 58 variables measured at admission, 11clinical and 3 radiological variables were associated with severity. Next, using 506,341chest CT images, we trained and evaluated deep learning models to segment thescans and reproduce radiologists' annotations. We also built CT image-based deeplearning models that predicted severity better than models based on the radiologists'reports. Finally, we showed that adding CT scan information—either throughradiologist lesion quantification or through deep learning—to clinical and biologicaldata, improves prediction of severity. These findings show that CT scans containnovel and unique prognostic information, which we included in a 6-variable ScanCovseverity score.
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|
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