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Journal Articles Radiology Year : 2021

Elastic Registration–driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT

Abstract

In patients with systemic sclerosis, a deep learning classifier applied to elastic registration of chest CT images depicted lung shrinkage and functional worsening with high accuracy. Background Longitudinal follow-up of interstitial lung diseases (ILDs) at CT mainly relies on the evaluation of the extent of ILD, without accounting for lung shrinkage. Purpose To develop a deep learning–based method to depict worsening of ILD based on lung shrinkage detection from elastic registration of chest CT scans in patients with systemic sclerosis (SSc). Materials and Methods Patients with SSc evaluated between January 2009 and October 2017 who had undergone at least two unenhanced supine CT scans of the chest and pulmonary function tests (PFTs) performed within 3 months were retrospectively included. Morphologic changes on CT scans were visually assessed by two observers and categorized as showing improvement, stability, or worsening of ILD. Elastic registration between baseline and follow-up CT images was performed to obtain deformation maps of the whole lung. Jacobian determinants calculated from the deformation maps were given as input to a deep learning–based classifier to depict morphologic and functional worsening. For this purpose, the set was randomly split into training, validation, and test sets. Correlations between mean Jacobian values and changes in PFT measurements were evaluated with the Spearman correlation. Results A total of 212 patients (median age, 53 years; interquartile range, 45–62 years; 177 women) were included as follows: 138 for the training set (65%), 34 for the validation set (16%), and 40 for the test set (21%). Jacobian maps demonstrated lung parenchyma shrinkage of the posterior lung bases in patients found to have worsened ILD at visual assessment. The classifier detected morphologic and functional worsening with an accuracy of 80% (32 of 40 patients; 95% confidence interval [CI]: 64%, 91%) and 83% (33 of 40 patients; 95% CI: 67%, 93%), respectively. Jacobian values correlated with changes in forced vital capacity (R = −0.38; 95% CI: −0.25, −0.49; P < .001) and diffusing capacity for carbon monoxide (R = −0.42; 95% CI: −0.27, −0.54; P < .001). Conclusion Elastic registration of CT scans combined with a deep learning classifier aided in the diagnosis of morphologic and functional worsening of interstitial lung disease in patients with systemic sclerosis.
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Dates and versions

hal-03134510 , version 1 (08-02-2021)

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Guillaume Chassagnon, Maria Vakalopoulou, Alexis Régent, Mihir Sahasrabudhe, Rafael Marini, et al.. Elastic Registration–driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT. Radiology, 2021, 298 (1), pp.189-198. ⟨10.1148/radiol.2020200319⟩. ⟨hal-03134510⟩
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