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Updated: Oct 31, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Study of Thoracic CT in COVID-19: The STOIC Project
Marie-Pierre Revel1, Samia Boussouar1, Constance de Margerie-Mellon1
1From the Department of Radiology, Université de Paris, APHP, Hôpital Cochin, 27 rue du Fg Saint Jacques, 75014 Paris, France (M.P.R., I.S., G.C., S. Bennani, F.B., S.D., C.H., C.J.); Department of Radiology, Sorbonne Université, APHP, Hôpital Pitié Salpétrière, Paris, France (S. Boussouar, A.R.); Department of Radiology, Université de Paris, APHP, Hôpital Saint-Louis, Paris, France (C.d.M.M.); Department of Radiology, Université Rennes 1, Hôpital Pontchaillou, Rennes, France (T.L., M.L.); Department of Radiology, Université Paris-Saclay, APHP, Hôpital Raymond Poincaré, Garches, France (D.M.); Department of Radiology, Sorbonne Université, APHP, Hôpital Tenon, Paris, France (A.M.); Department of Radiology, Université de Strasbourg, Hôpital de Hautepierre, Strasbourg, France (S.M.); Department of Radiology, Université de Paris, APHP, Hôpital Bichat, Paris, France (M.P.D., A.K.); Department of Radiology, Université de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France (M.O.); Department of Radiology, Université de Montpellier, Hôpital Arnaud de Villeneuve, Montpellier France (S. Bommart); Department of Radiology, Université Paris-Saclay, APHP, Hôpital Ambroise Paré, Boulogne, France (M.E.H.); Department of Radiology, Université de Lorraine, Hôpital Brabois, Vandoeuvre, France (I.P.); Department of Radiology, Université de Paris, APHP, Hôpital Européen Georges Pompidou, INSERM U970, PARCC, Paris, France (L.F.); Department of Radiology, Sorbonne Université, APHP, Hôpital Avicenne, Bobigny, France (P.Y.B.); Department of Radiology, Université Paris-Saclay, APHP, Hôpital Bicêtre, Le Kremlin-Bicêtre, France (M.F.B.); Department of Radiology, Université Paris-Saclay, APHP, Hôpital Antoine Béclère, Clamart, France (L.R.); Department of Radiology, Université de Paris, APHP, Hôpital Lariboisière, Paris, France (V.B.); Department of Radiology, Université Claude Bernard Lyon 1, Hospices Civils de Lyon, Hôpital Lyon Sud, Pierre-Benite, France (P.R.); Department of Radiology, Université de Paris, APHP, Hôpital Beaujon, Clichy, France (J.G.); Department of Radiology, Université Paris Est, APHP, Hôpital Henri Mondor, Créteil, France (J.F.D.); Departments of Radiology (E.D.) and Clinical Epidemiology (R.P.), Université de Paris, APHP, Hôtel-Dieu, Paris, France; Sorbonne Université, APHP, Hôpital Avicenne, Department of Pneumology, INSERM UMR 1272, Bobigny, France (D.V.); and Université de Paris APHP, Clinical Research Unit Paris Centre, Paris, France (L.J., H.A.).
Chest CT scans show good diagnostic performance for COVID-19 pneumonia, with 80% sensitivity and specificity. The extent of pneumonia on CT is a key predictor of severe outcomes, improving prognostic models.
Area of Science:
- Radiology
- Infectious Diseases
- Pulmonology
Background:
- Conflicting data exist on chest CT's diagnostic accuracy for COVID-19 pneumonia.
- Disease extent on CT scans may influence patient prognosis.
Purpose of the Study:
- To establish a large, publicly available dataset for COVID-19 pneumonia research.
- To evaluate the diagnostic and prognostic capabilities of chest CT in COVID-19 pneumonia.
Main Methods:
- A multicenter, retrospective cohort study of 10,735 patients undergoing chest CT and RT-PCR testing.
- CT images were interpreted blinded to clinical data and RT-PCR results.
- Multivariable logistic regression was used to predict severe outcomes.
Main Results:
- Chest CT demonstrated 80.2% sensitivity and 79.7% specificity for COVID-19 pneumonia compared to RT-PCR.
- Radiologist experience did not significantly impact diagnostic accuracy (Gwet AC1 coefficient, 0.79).
- Extent of pneumonia on CT was the strongest predictor of severe outcomes (OR, 3.25), improving prognostic models.
Conclusions:
- Chest CT, interpreted using predefined criteria, offers reliable diagnostic performance for COVID-19 pneumonia.
- CT findings, particularly disease extent, are valuable for predicting 1-month severe outcomes.
- The study provides a valuable dataset for future research on COVID-19 pneumonia.
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