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Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

ABSTRACT: The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

SUBMITTER: Lassau N

PROVIDER: S-EPMC7840774 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

Lassau Nathalie N Ammari Samy S Chouzenoux Emilie E Gortais Hugo H Herent Paul P Devilder Matthieu M Soliman Samer S Meyrignac Olivier O Talabard Marie-Pauline MP Lamarque Jean-Philippe JP Dubois Remy R Loiseau Nicolas N Trichelair Paul P Bendjebbar Etienne E Garcia Gabriel G Balleyguier Corinne C Merad Mansouria M Stoclin Annabelle A Jegou Simon S Griscelli Franck F Tetelboum Nicolas N Li Yingping Y Verma Sagar S Terris Matthieu M Dardouri Tasnim T Gupta Kavya K Neacsu Ana A Chemouni Frank F Sefta Meriem M Jehanno Paul P Bousaid Imad I Boursin Yannick Y Planchet Emmanuel E Azoulay Mikael M Dachary Jocelyn J Brulport Fabien F Gonzalez Adrian A Dehaene Olivier O Schiratti Jean-Baptiste JB Schutte Kathryn K Pesquet Jean-Christophe JC Talbot Hugues H Pronier Elodie E Wainrib Gilles G Clozel Thomas T Barlesi Fabrice F Bellin Marie-France MF Blum Michael G B MGB

Nature communications 20210127 1

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to th ...[more]

PMID: 33504775

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Project description:PurposeHigh-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework.MethodsA total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA. All the pneumonia lesions on CT images were manually delineated by four radiologists. One hundred eighty-four patients (n = 93 COVID-19 positive; n = 91 COVID-19 negative; 24,216 pneumonia lesions from 12,001 CT image slices) from two hospitals from China served as discovery cohort for model development. Thirty-two patients (17 COVID-19 positive, 15 COVID-19 negative; 7883 pneumonia lesions from 3799 CT image slices) from a US hospital served as external validation cohort. A bi-directional adversarial network-based framework and PyRadiomics package were used to extract deep learning and radiomics features, respectively. Linear and Lasso classifiers were used to develop models predictive of COVID-19 versus non-COVID-19 viral pneumonia.Results120-dimensional deep learning image features and 120-dimensional radiomics features were extracted. Linear and Lasso classifiers identified 32 high-dimensional deep learning image features and 4 radiomics features associated with COVID-19 pneumonia diagnosis (P < 0.0001). Both models achieved sensitivity > 73% and specificity > 75% on external validation cohort with slight superior performance for radiomics Lasso classifier. Human expert diagnostic performance improved (increase by 16.5% and 11.6% in sensitivity and specificity, respectively) when using a combined deep learning-radiomics model.ConclusionsWe uncover specific deep learning and radiomics features to add insight into interpretability of machine learning algorithms and compare deep learning and radiomics models for COVID-19 pneumonia that might serve to augment human diagnostic performance.

Dataset Information

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

Publications

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

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