Deep Learning–based Approach for Automated Assessment of Interstitial Lung Disease in Systemic Sclerosis on CT Images

Autor: Guillaume Chassagnon, Maria Vakalopoulou, Luc Mouthon, Rafael Marini, Naim Jerjir, Galit Aviram, Norbert Bus, Evangelia I. Zacharaki, Arsène Mekinian, Nikos Paragios, Marie-Pierre Revel, Charlotte Martin, Thong Hua-Huy, Anh Tuan Dinh-Xuan, Alexis Régent, Nouria Benmostefa, Laurence Monnier-Cholley
Přispěvatelé: Service de Radiologie [CHU Cochin], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), OPtimisation Imagerie et Santé (OPIS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, Mathématiques et Informatique pour la Complexité et les Systèmes (MICS), CentraleSupélec-Université Paris-Saclay, Service de médecine interne et centre de référence des maladies rares [CHU Cochin], Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay, Tel Aviv Sourasky Medical Center [Te Aviv], TheraPanacea [Paris], Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Radiology: Artificial Intelligence
Radiology: Artificial Intelligence, 2020, 2 (4), pp.e190006. ⟨10.1148/ryai.2020190006⟩
Radiol Artif Intell
Radiology: Artificial Intelligence, RSNA, 2020, 2 (4), pp.e190006. ⟨10.1148/ryai.2020190006⟩
ISSN: 2638-6100
Popis: International audience; Abstract :The reported deep learning–based method can be used to evaluate the extent of interstitial lung disease in systemic sclerosis with results comparable to those of radiologists.Purpose :To develop a deep learning algorithm for the automatic assessment of the extent of systemic sclerosis (SSc)–related interstitial lung disease (ILD) on chest CT images.Materials and Methods :This retrospective study included 208 patients with SSc (median age, 57 years; 167 women) evaluated between January 2009 and October 2017. A multicomponent deep neural network (AtlasNet) was trained on 6888 fully annotated CT images (80% for training and 20% for validation) from 17 patients with no, mild, or severe lung disease. The model was tested on a dataset of 400 images from another 20 patients, independently partially annotated by three radiologist readers. The ILD contours from the three readers and the deep learning neural network were compared by using the Dice similarity coefficient (DSC). The correlation between disease extent obtained from the deep learning algorithm and that obtained by using pulmonary function tests (PFTs) was then evaluated in the remaining 171 patients and in an external validation dataset of 31 patients based on the analysis of all slices of the chest CT scan. The Spearman rank correlation coefficient (ρ) was calculated to evaluate the correlation between disease extent and PFT results.Results :The median DSCs between the readers and the deep learning ILD contours ranged from 0.74 to 0.75, whereas the median DSCs between contours from radiologists ranged from 0.68 to 0.71. The disease extent obtained from the algorithm, by analyzing the whole CT scan, correlated with the diffusion lung capacity for carbon monoxide, total lung capacity, and forced vital capacity (ρ = −0.76, −0.70, and −0.62, respectively; P < .001 for all) in the dataset for the correlation with PFT results. The disease extents correlated with diffusion lung capacity for carbon monoxide, total lung capacity, and forced vital capacity were ρ = −0.65, −0.70, and −0.57, respectively, in the external validation dataset (P < .001 for all).Conclusion :The developed algorithm performed similarly to radiologists for disease-extent contouring, which correlated with pulmonary function to assess CT images from patients with SSc-related ILD.
Databáze: OpenAIRE
Externí odkaz: