Rationale and Objectives
This article aims to explore the potential use of lung texture assessed in CT images
in distinguishing between the usual interstitial pneumonia and the nonspecific interstitial
pneumonia.
Materials and Methods
A retrospective analysis of 96 cases of interstitial pneumonia was performed. Among
these cases, there were 40 cases of usual interstitial pneumonia (UIP) and 56 cases
of the nonspecific interstitial pneumonia (NSIP) . All of the patients underwent computed
tomography (CT) scans. A lung intelligence kit (LK) was utilized to perform lung segmentation
and texture feature extraction. The significant variables were determined by variance
analysis, least absolute shrinkage and selection operator (LASSO) and multivariate
logistic regression. Finally, a multivariate logistic regression model was established
to distinguish between the two types of interstitial pneumonia. Receiver operating
characteristic (ROC) curves, area under the curve (AUC) values, sensitivity, and specificity
were used to evaluate the performance of the established model.
Results
A total of 100 texture features were extracted from the whole lung that was segmented
by LK, and 8 features remained after feature reduction. The AUC, sensitivity, and
specificity of the multivariate logistic regression model in the training group and
the test group were 0.952 and 0.838, 0.821 and 0.667, and 0.949 and 0.824, respectively.
Conclusion
It is possible to distinguish between UIP and NSIP using lung texture features obtained
from CT images.
Key Words
Abbreviations:
UIP (usual interstitial pneumonia), NSIP (nonspecific interstitial pneumonia), ROC (receiver operating characteristic), AUC (area under the curve), LK (Lung Intelligence Kit)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: July 14, 2022
Accepted:
June 15,
2022
Received in revised form:
June 9,
2022
Received:
March 30,
2022
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
