TY - GEN
T1 - Transferring CT image biomarkers from fibrosing idiopathic interstitial pneumonia to COVID-19 analysis
AU - Fetita, Catalin
AU - Rennotte, Simon
AU - Latrasse, Marjorie
AU - Tapu, Ruxandra
AU - Maury, Mathilde
AU - Mocanu, Bogdan
AU - Nunes, Hilario
AU - Brillet, Pierre Yves
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Fibrosing idiopathic interstitial pneumonia (fIIP) is a subclass of interstitial lung diseases, which leads to fibrosis in a continuous and irreversible process of lung function decay. Patients with fIIP require regular quantitative follow-up with CT and several image biomarkers have already been proposed to grade the pathology severity and try to predict the evolution. Among them, we cite the spatial extent of the diseased lung parenchyma and airway and vascular remodeling markers. COVID-19 (Cov-19) presents several similarities with fIIP and this condition is moreover suspected to evolve to fIIP in 10-30% of severe cases. Note also that the main difference between Cov-19 and fIIP is the presence of peripheral ground glass opacities and less or no amount of fibrosis in the lung, as well as the absence of airway remodeling. This paper proposes a preliminary study to investigate how existing image markers for fIIP may apply to Cov-19 phenotyping, namely texture classification and vascular remodeling. In addition, since for some patients, the fIIP/Cov-19 follow-up protocol imposes CT acquisitions at both full inspiration and full expiration, this information could also be exploited to extract additional knowledge for each individual case. We hypothesize that taking into account the two respiratory phases to analyze breathing parameters through interpolation and registration might contribute to a better phenotyping of the pathology. This preliminary study, conducted on a reduced number of patients (eight Cov-19 of different severity degrees, two fIIP patients and one control), shows a great potential of the selected CT image markers.
AB - Fibrosing idiopathic interstitial pneumonia (fIIP) is a subclass of interstitial lung diseases, which leads to fibrosis in a continuous and irreversible process of lung function decay. Patients with fIIP require regular quantitative follow-up with CT and several image biomarkers have already been proposed to grade the pathology severity and try to predict the evolution. Among them, we cite the spatial extent of the diseased lung parenchyma and airway and vascular remodeling markers. COVID-19 (Cov-19) presents several similarities with fIIP and this condition is moreover suspected to evolve to fIIP in 10-30% of severe cases. Note also that the main difference between Cov-19 and fIIP is the presence of peripheral ground glass opacities and less or no amount of fibrosis in the lung, as well as the absence of airway remodeling. This paper proposes a preliminary study to investigate how existing image markers for fIIP may apply to Cov-19 phenotyping, namely texture classification and vascular remodeling. In addition, since for some patients, the fIIP/Cov-19 follow-up protocol imposes CT acquisitions at both full inspiration and full expiration, this information could also be exploited to extract additional knowledge for each individual case. We hypothesize that taking into account the two respiratory phases to analyze breathing parameters through interpolation and registration might contribute to a better phenotyping of the pathology. This preliminary study, conducted on a reduced number of patients (eight Cov-19 of different severity degrees, two fIIP patients and one control), shows a great potential of the selected CT image markers.
KW - COVID-19
KW - Convolutional networks
KW - Deep learning
KW - Fibrosing idiopathic interstitial pneumonia
KW - Image biomarker
KW - Infiltrative lung diseases
KW - Lung deformation
KW - Lung texture classification
KW - Vascular remodeling
U2 - 10.1117/12.2580658
DO - 10.1117/12.2580658
M3 - Conference contribution
AN - SCOPUS:85103687662
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2021
A2 - Mazurowski, Maciej A.
A2 - Drukker, Karen
PB - SPIE
T2 - Medical Imaging 2021: Computer-Aided Diagnosis
Y2 - 15 February 2021 through 19 February 2021
ER -