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Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution.
Djahnine, Aissam; Lazarus, Carole; Lederlin, Mathieu; Mulé, Sébastien; Wiemker, Rafael; Si-Mohamed, Salim; Jupin-Delevaux, Emilien; Nempont, Olivier; Skandarani, Youssef; De Craene, Mathieu; Goubalan, Segbedji; Raynaud, Caroline; Belkouchi, Younes; Afia, Amira Ben; Fabre, Clement; Ferretti, Gilbert; De Margerie, Constance; Berge, Pierre; Liberge, Renan; Elbaz, Nicolas; Blain, Maxime; Brillet, Pierre-Yves; Chassagnon, Guillaume; Cadour, Farah; Caramella, Caroline; Hajjam, Mostafa El; Boussouar, Samia; Hadchiti, Joya; Fablet, Xavier; Khalil, Antoine; Talbot, Hugues; Luciani, Alain; Lassau, Nathalie; Boussel, Loic.
Afiliación
  • Djahnine A; Philips Research France, 92150 Suresnes, France; CREATIS, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France. Electronic address: aissam.djahnine@philips.com.
  • Lazarus C; Philips Research France, 92150 Suresnes, France.
  • Lederlin M; Department of Radiology, CHU Rennes, 35000 Rennes, France.
  • Mulé S; Medical Imaging Department, Henri Mondor University Hospital, AP-HP, Créteil, France, Inserm, U955, Team 18, 94000 Créteil, France.
  • Wiemker R; Philips Research France, 92150 Suresnes, France.
  • Si-Mohamed S; Department of Radiology, Hospices Civils de Lyon, 69500 Lyon, France.
  • Jupin-Delevaux E; Department of Radiology, Hospices Civils de Lyon, 69500 Lyon, France.
  • Nempont O; Philips Research France, 92150 Suresnes, France.
  • Skandarani Y; Philips Research France, 92150 Suresnes, France.
  • De Craene M; Philips Research France, 92150 Suresnes, France.
  • Goubalan S; Philips Research France, 92150 Suresnes, France.
  • Raynaud C; Philips Research France, 92150 Suresnes, France.
  • Belkouchi Y; Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, BIOMAPS, UMR 1281, Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; OPIS - Optimisation Imagerie et Santé, Université Paris-Saclay, Inria, CentraleSupélec, CVN - Centre de vision numérique, 91190 Gif-Sur-Yvette, France.
  • Afia AB; Department of Radiology, APHP Nord, Hôpital Bichat, 75018 Paris, France.
  • Fabre C; Department of Radiology, Centre Hospitalier de Laval, 53000 Laval, France.
  • Ferretti G; Universite Grenobles Alpes, Service de Radiologie et Imagerie Médicale, CHU Grenoble-Alpes, 38000 Grenoble, France.
  • De Margerie C; Université Paris Cité, 75006 Paris, France, Department of Radiology, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, 75010 Paris, France.
  • Berge P; Department of Radiology, CHU Angers, 49000 Angers, France.
  • Liberge R; Department of Radiology, CHU Nantes, 44000 Nantes, France.
  • Elbaz N; Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France.
  • Blain M; Department of Radiology, Hopital Henri Mondor, AP-HP, 94000 Créteil, France.
  • Brillet PY; Department of Radiology, Hôpital Avicenne, Paris 13 University, 93000 Bobigny, France.
  • Chassagnon G; Department of Radiology, Hopital Cochin, APHP, 75014 Paris, France; Université Paris Cité, 75006 Paris, France.
  • Cadour F; APHM, Hôpital Universitaire Timone, CEMEREM, 13005 Marseille, France.
  • Caramella C; Department of Radiology, Groupe Hospitalier Paris Saint-Joseph, 75015 Paris, France.
  • Hajjam ME; Department of Radiology, Hôpital Ambroise Paré Hospital, UMR 1179 INSERM/UVSQ, Team 3, 92100 Boulogne-Billancourt, France.
  • Boussouar S; Sorbonne Université, Hôpital La Pitié-Salpêtrière, APHP, Unité d'Imagerie Cardiovasculaire et Thoracique (ICT), 75013 Paris, France.
  • Hadchiti J; Department of Imaging, Institut Gustave Roussy, Université Paris-Saclay. 94800 Villejuif, France.
  • Fablet X; Department of Radiology, CHU Rennes, 35000 Rennes, France.
  • Khalil A; Department of Radiology, APHP Nord, Hôpital Bichat, 75018 Paris, France.
  • Talbot H; OPIS - Optimisation Imagerie et Santé, Université Paris-Saclay, Inria, CentraleSupélec, CVN - Centre de vision numérique, 91190 Gif-Sur-Yvette, France.
  • Luciani A; Medical Imaging Department, Henri Mondor University Hospital, AP-HP, Créteil, France, Inserm, U955, Team 18, 94000 Créteil, France.
  • Lassau N; Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, BIOMAPS, UMR 1281, Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, Université Paris-Saclay. 94800 Villejuif, France.
  • Boussel L; CREATIS, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France; Department of Radiology, Hospices Civils de Lyon, 69500 Lyon, France.
Diagn Interv Imaging ; 105(3): 97-103, 2024 Mar.
Article en En | MEDLINE | ID: mdl-38261553
ABSTRACT

PURPOSE:

The purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed tomography pulmonary angiography (CTPA) examinations with limited annotations. MATERIALS AND

METHODS:

Using a database of 3D CTPA examinations of 1268 patients with image-level annotations, and two other public datasets of CTPA examinations from 91 (CAD-PE) and 35 (FUME-PE) patients with pixel-level annotations, a pipeline consisting of (i), detecting blood clots; (ii), performing PE-positive versus negative classification; (iii), estimating the Qanadli score; and (iv), predicting RV/LV diameter ratio was followed. The method was evaluated on a test set including 378 patients. The performance of PE classification and severity quantification was quantitatively assessed using an area under the curve (AUC) analysis for PE classification and a coefficient of determination (R²) for the Qanadli score and the RV/LV diameter ratio.

RESULTS:

Quantitative evaluation led to an overall AUC of 0.870 (95% confidence interval [CI] 0.850-0.900) for PE classification task on the training set and an AUC of 0.852 (95% CI 0.810-0.890) on the test set. Regression analysis yielded R² value of 0.717 (95% CI 0.668-0.760) and of 0.723 (95% CI 0.668-0.766) for the Qanadli score and the RV/LV diameter ratio estimation, respectively on the test set.

CONCLUSION:

This study shows the feasibility of utilizing AI-based assistance tools in detecting blood clots and estimating PE severity scores with 3D CTPA examinations. This is achieved by leveraging blood clots and cardiac segmentations. Further studies are needed to assess the effectiveness of these tools in clinical practice.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Embolia Pulmonar / Trombosis / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Diagn Interv Imaging Año: 2024 Tipo del documento: Article Pais de publicación: Francia
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Embolia Pulmonar / Trombosis / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Diagn Interv Imaging Año: 2024 Tipo del documento: Article Pais de publicación: Francia