Exploring the Value of Lung Texture Features in Distinguishing Usual and Non-specific Interstitial Pneumonia

Abstract

\n Backgground At present, the most common types of interstitial pneumonia are usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP), and different types have different prognosis. In addition, if there is a mixture of different classifications, it will be difficult for radiologists to diagnose, and it will make clinical treatment difficult. Therefore, clinicians urgently need new imaging methods to solve such problems. This article aims to explore the CT lung texture images of UIP and NSIP to provide evidence for the identification of UIP and NSIP. Methods A retrospective analysis of 96 cases of interstitial pneumonia diagnosed by the Department of Pathology and the Affiliated Hospital of Guilin Medical College. Among them, there are 40 cases of UIP and 56 cases of NSIP. All patients are scanned by CT. Lung Intelligence Kit was utilized to perform lung segmentation and texture feature extraction. Variance analysis, least absolute shrinkage and selection operator (Lasso) and multivariate logistic regression were used to select effective features. Finally, a multivariate logistic regression model was constructed to identify two kinds of interstitial pneumonia. Receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, specificity were used to evaluate performance of the constructed model. We used the LK software to segment the two sets of lungs. Feature calculation and selection were performed on the data of the two groups of interstitial pneumonia after lung segmentation, the logistic regression model was established for the selected features, and the ROC curve was drawn. Results A total of 100 texture features are extracted from the whole lung segmented by LK, and finally 8 features are left after feature reduction. The above-mentioned values of UIP and NSIP of the training group are greater than those of the test group. Conclusions It is possible to distinguish UIP and NSIP by using LK software to extract lung texture in CT images.