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Persistent Pure Ground-Glass Nodules Larger Than 5 mm: Differentiation of Invasive Pulmonary Adenocarcinomas From Preinvasive Lesions or Minimally Invasive Adenocarcinomas Using Texture Analysis.
- Source :
-
Investigative radiology [Invest Radiol] 2015 Nov; Vol. 50 (11), pp. 798-804. - Publication Year :
- 2015
-
Abstract
- Objective: To evaluate the differentiating potentials of computed tomography texture analysis for invasive pulmonary adenocarcinomas (IPAs) from their preinvasive lesions or minimally invasive adenocarcinomas (MIAs) manifesting as persistent pure ground-glass nodules (PGGNs) larger than 5 mm.<br />Materials and Methods: This institutional review board-approved retrospective study included 63 patients (23 men and 40 women) with 66 PGGNs larger than 5 mm on unenhanced computed tomography from 2005 to 2013. All PGGNs were pathologically confirmed and categorized into 2 groups [IPAs (n = 11) vs preinvasive lesions (n = 41)/MIAs (n = 14)]. Each PGGN was segmented manually, and their texture features were quantitatively extracted. To identify significant differentiating factors of IPAs from preinvasive lesions/MIAs, multivariate logistic regression and C-statistic analyses were performed.<br />Results: Between IPAs and preinvasive lesions/MIAs, nodule size, volume, mass, entropy, effective diameter, and surface area were significantly different (P < 0.05), and homogeneity and gray level co-occurrence matrix inverse difference moment showed marginal significance (P < 0.10). Subsequent multivariate analysis revealed larger nodule mass [adjusted odds ratio (OR), 11.92], higher entropy (adjusted OR, 35.12), and lower homogeneity (adjusted OR, 0.278 × 10) as independent differentiating factors of IPAs. Subgroup analysis showed that larger nodule mass, higher entropy, and lower homogeneity were also significant differentiating variables of IPAs in nodules of diameter 10 mm or larger. A multiple logistic regression model using these features showed excellent [area under the curve (AUC), 0.962] and significantly higher differentiating performance compared to nodule size (AUC, 0.712) or mass (AUC, 0.788) alone.<br />Conclusion: Computed tomography texture features such as higher entropy and lower homogeneity were significant differentiating factors of IPAs presenting as PGGNs larger than 5 mm and have potentials to enhance the differentiating performance.
- Subjects :
- Adenocarcinoma diagnostic imaging
Adult
Aged
Algorithms
Diagnosis, Differential
Female
Humans
Imaging, Three-Dimensional methods
Lung Neoplasms diagnostic imaging
Male
Middle Aged
Neoplasm Invasiveness
Radiographic Image Enhancement methods
Reproducibility of Results
Sensitivity and Specificity
Solitary Pulmonary Nodule diagnostic imaging
Adenocarcinoma pathology
Lung Neoplasms pathology
Pattern Recognition, Automated methods
Radiographic Image Interpretation, Computer-Assisted methods
Solitary Pulmonary Nodule pathology
Tomography, X-Ray Computed methods
Subjects
Details
- Language :
- English
- ISSN :
- 1536-0210
- Volume :
- 50
- Issue :
- 11
- Database :
- MEDLINE
- Journal :
- Investigative radiology
- Publication Type :
- Academic Journal
- Accession number :
- 26146871
- Full Text :
- https://doi.org/10.1097/RLI.0000000000000186