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Πέμπτη 24 Αυγούστου 2017

Lepidic Predominant Pulmonary Lesions (LPL)

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Publication date: Available online 24 August 2017
Source:Academic Radiology
Author(s): Jeffrey B. Alpert, Henry Rusinek, Jane P. Ko, Bari Dane, Harvey I. Pass, Bernard K. Crawford, Amy Rapkiewicz, David P. Naidich
Rationale and ObjectivesThis study aimed to differentiate pathologically defined lepidic predominant lesions (LPL) from more invasive adenocarcinomas (INV) using three-dimensional (3D) volumetric density and first-order texture histogram analysis of surgically excised stage 1 lung adenocarcinomas.Materials and MethodsThis retrospective study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Sixty-four cases of pathologically proven stage 1 lung adenocarcinoma surgically resected between September 2006 and October 2015, including LPL (n = 43) and INV (n = 21), were evaluated using high-resolution computed tomography. Quantitative measurements included nodule volume, percent solid volume (% solid), and first-order texture histogram analysis including skewness, kurtosis, entropy, and mean nodule attenuation within each histogram quartile. Binomial logistic regression models were used to identify the best set of parameters distinguishing LPL from INV.ResultsUnivariate analysis of 3D volumetric density and histogram features was statistically significant between LPL and INV groups (P < .05). Accuracy of a binomial logistic model to discriminate LPL from INV based on size and % solid was 85.9%. With optimized probability cutoff, the model achieves 81% sensitivity, 76.7% specificity, and area under the receiver operating characteristic curve of 0.897 (95% confidence interval, 0.821–0.973). An additional model based on size and mean nodule attenuation of the third quartile (Hu_Q3) of the histogram achieved similar accuracy of 81.3% and area under the receiver operating characteristic curve of 0.877 (95% confidence interval, 0.790–0.964).ConclusionsBoth 3D volumetric density and first-order texture analysis of stage 1 lung adenocarcinoma allow differentiation of LPL from more invasive adenocarcinoma with overall accuracy of 85.9%–81.3%, based on multivariate analyses of either size and % solid or size and Hu_Q3, respectively.



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