کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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5726334 | 1609730 | 2017 | 8 صفحه PDF | دانلود رایگان |
- We proposed a new method for quantitative analysis of computed tomography (CT).
- This method correlated well with the histological grading of pulmonary fibrosis.
- We measured subpleural fibrotic lesion volume (H2) on CT using this system.
- H2 predicted the diagnosis of idiopathic pulmonary fibrosis with 0.72 of accuracy.
- H2 correlated significantly with patients survival (p = 0.011).
PurposeTo compare computer-aided diagnostic results with histological findings obtained by surgical biopsy and evaluate whether subpleural lesion volumes can aid identification of idiopathic pulmonary fibrosis (IPF).Materials andMethodsWe retrospectively analyzed computed tomography (CT) images of 79 patients (43 with fibrosing nonspecific interstitial pneumonia (fNSIP) and 36 with IPF) using the Gaussian Histogram Normalized Correlation (GHNC) system. We determined the H-pattern based on honeycomb and/or fibrosis with traction bronchiectasis on CT, and measured the H-pattern volume ratio at the biopsy sites and in the subpleural area. The biopsy site CT data were compared with biopsy specimens using Spearman's correlation. H-pattern volumes in the subpleural area within 2 mm under the pleura (H2) were analyzed to predict IPF diagnosis and patients prognosis.ResultsThe H-pattern volume ratio at the biopsy sites showed significant correlation with histological honeycomb (r = 0.355, p < 0.001), subpleural collapse (r = 0.410, p < 0.001), and heterogeneity (r = 0.484, p < 0.001). Multivariate regression analysis, adjusting for age, sex, and CT results, revealed that the H2 was a significant independent predictor of IPF diagnosis (odds ratio: 1.073; p = 0.048). H2 correlated with patients' survival after adjusting for age (p = 0.003).ConclusionThe computer-aided H-pattern volume ratio of the subpleural area indicates subpleural abnormalities quantitatively and may help diagnose IPF.
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Journal: European Journal of Radiology - Volume 90, May 2017, Pages 106-113