Bone Evaluation by High-Resolution Peripheral Quantitative Computed Tomography in Patients With Adrenal Incidentaloma

CONTEXT

Data regarding high-resolution peripheral quantitative computed tomography (HR-pQCT) in patients with adrenal incidentaloma (AI) are unknown.

PURPOSE

To evaluate the areal bone mineral density (aBMD), microstructure, and fractures in patients with nonfunctioning AI (NFAI) and autonomous cortisol secretion (ACS).

METHODS

We evaluated 45 patients with NFAI (1 mg dexamethasone suppression test [DST] ≤1.8 µg/dL) and 30 patients with ACS (1 mg DST 1.9-5.0 µg/dL). aBMD was measured using dual-energy X-ray absorptiometry; vertebral fracture by spine X-ray; and bone geometry, volumetric bone mineral density (vBMD), and microstructure by HR-pQCT.

RESULTS

Patients with ACS showed lower aBMD values at the spine, femoral neck, and radius 33% than those with NFAI. Osteoporosis was frequent in both groups: NFAI (64.9%) and ACS (75%). Parameters at the distal radius by HR-pQCT were decreased in patients with ACS compared to those with NFAI: trabecular vBMD (Tb.vBMD, P = 0.03), inner zone of the trabecular region (Inn.Tb.vBMD, P = 0.01), the bone volume/tissue volume ratio (BV/TV, P = 0.03) and trabecular thickness (P = 0.04). As consequence, a higher ratio of the outer zone of the trabecular region/inner zone vBMD (Meta/Inn.vBMD, P = 0.003) was observed. A correlation between the cortisol levels after 1 mg DST and Meta/Inn.vBMD ratio was found (r = 0.29; P = 0.01). The fracture frequency was 73.7% in patients with ACS vs 55.6% in patients with NFAI (P = 0.24).

CONCLUSION

Our findings point to an association between trabecular bone microarchitectural derangement at the distal radius and ACS. Our data suggest that AI have a negative impact on bone when assessed by HR-pQCT, probably associated to subclinical hypercortisolism.

On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms

The quantification of fat depots on the surroundings of the heart is an accurate procedure for evaluating health risk factors correlated with several diseases. However, this type of evaluation is not widely employed in clinical practice due to the required human workload. This work proposes a novel technique for the automatic segmentation of cardiac fat pads. The technique is based on applying classification algorithms to the segmentation of cardiac CT images. Furthermore, we extensively evaluate the performance of several algorithms on this task and discuss which provided better predictive models. Experimental results have shown that the mean accuracy for the classification of epicardial and mediastinal fats has been 98.4% with a mean true positive rate of 96.2%. On average, the Dice similarity index, regarding the segmented patients and the ground truth, was equal to 96.8%. Therfore, our technique has achieved the most accurate results for the automatic segmentation of cardiac fats, to date.