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Measurement of bronchial lumen and wall thickness of large and small airways from CT images is useful in clinical practice for the characterization of several diseases. Automatic methods are high desirable to save reporting time and to improve measurement reproducibility. In this study airways segmentation was performed by the application of a multi-step algorithm including airways segmentation, scheletonization and classification of the bronchial three, and calculation of the bronchial diameter and the airway wall thickness. The proposed method was validated on images from ten patients with suspect airways disease, comparing measurements obtained by the proposed algorithm with manual measurements performed by two expert users. Wall thickness and lumen diameter automatic measurements performed by the proposed approach were interchangeable with manual measurements, in the sense that the difference in measurements between the automatic method and the manual analysis is equivalent to the inter-observer variability. The proposed approach thanks to their simplicity showed computational efficiency, reliability, and was demonstrated to be totally operator-independent.

Aims Gamma-glutamyltransferase (GGT) has been recognized as a cardiovascular risk factor, and its highest molecular weight fraction [big GGT (b-GGT)] is found in vulnerable atherosclerotic plaques. We explored the relationship between b-GGT, computed tomography findings, and long-term outcomes in the general population. Methods and results Between May 2010 and October 2011, subjects aged 45–75 years living in a Tuscan city and without known cardiac disease were screened. The primary endpoint was a composite of cardiovascular death or acute coronary syndrome requiring urgent coronary revascularization. Gamma-glutamyltransferase fractions were available in 898 subjects [median age 65 years (25th–75th percentile 55–70), 46% men]. Median plasma GGT was 20 IU (15–29), and b-GGT was 2.28 (1.28–4.17). Coronary artery calcium (CAC) score values were 0 (0–60), and the volume of pro-atherogenic epicardial fat was 155 mL (114–204). In a model including age, sex, low-density lipoprotein (LDL) cholesterol, current or previous smoking status, hypertension, diabetes, obesity, b-GGT independently predicted epicardial fat volume (EFV) (r = 0.162, P < 0.001), but not CAC (P = 0.198). Over a 10.3-year follow-up (9.6–10.8), 27 subjects (3%) experienced the primary endpoint. We evaluated couples of variables including b-GGT and a cardiovascular risk factor, CAC or EFV. Big GGT yielded independent prognostic significance from age, LDL cholesterol, current or previous smoking status, hypertension, diabetes, obesity, but not CAC or EFV. Conversely, GGT predicted the primary endpoint even independently from CAC and EFV. Conclusion Big GGT seemed at least as predictive as the commonly available GGT assay; therefore, the need for b-GGT rather than GGT measurement should be carefully examined.

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