Effect of Metabolic Syndrome on Anatomy and Function of the Lower Urinary Tract Assessed on MRI

OBJECTIVE

To investigate the relationship between metabolic syndrome (MetS) and lower urinary tract symptoms (LUTS) with functional and anatomic changes of the lower urinary tract with MRI.

MATERIALS AND METHODS

The bladder and prostate of 95 subjects (56M, 39F) were segmented on T2-weighted pelvic MRI using Materialize Mimics 3D software. Bladder wall volume (BWV), post-void residual (PVR) and prostate volume (PV) were quantified from the 3D renderings. LUTS were quantified using validated questionnaires administered at the time of MRI. Wilcoxin rank sum, win ratio and chi-square tests were used to correlate symptom scores, BWV, PVR and PV in patients 1) without vs with MetS, 2) with mild (IPSS or UDI-6: 0-7) vs moderate-severe (IPSS: 8-35 or UDI-6: ≥8) and 3) normal vs enlarged prostates (>40cm³). Multivariate linear regression was used to determine predictors for BWV, PVR and PV.

RESULTS

Men with MetS had increased BWV (66.8 vs 51.1cm³, P = .003), higher PVR (69.1 vs 50.5cc, P= .05) and increased PV (67.2 vs 40.1cm³, P= .01). Women without and with MetS had similar BWV, PVR and LUTS (P= .3-.78). There was no difference in prevalence of MetS, BWV, PVR or PV in men or women with mild vs moderate-severe LUTS (P = .26-.97). Men with enlarged prostates were more likely to have MetS (P = .003). There was no difference in BWV, PVR and LUTS for men with normal vs enlarged prostates (P= .44-.94). In men, BWV was highly correlated with MetS (P = .005) on regression analysis.

CONCLUSION

MetS leads to detrusor hypertrophy and may contribute to impaired bladder function, likely related to the effect on the prostate.

Pharmacokinetics of panduratin A following oral administration of a Boesenbergia pandurata extract to rats

Boesenbergia pandurata and its major active ingredient, panduratin A (PAN), exhibit antibacterial, anti-oxidant, anti-inflammatory, and anti-obesity effects. We explored the time course of the plasma and tissue (in the major organs, gums and skin) concentrations of PAN after oral administration of a B. pandurata extract to rats. Model-dependent analysis was used to quantify the skin distribution of PAN after systemic exposure. The PAN level peaked at 1.12 ± 0.22 μg/mL after 3 h, and then biexponentially decayed with a terminal half-life of 9 h. The mean clearance (Cl/F) was 2.33 ± 0.68 L/h/kg. The PAN levels in organs were in the following order (highest first): skin, lung, heart, gum, liver, spleen, kidney, and brain. For the first time, the time course of PAN levels in plasma and organs was investigated after oral administration of a BPE. This study helps to explain the pharmacological activities of PAN in the skin and gums. The pharmacokinetic model provided data in the plasma and skin concentrations of PAN, which are of fundamental importance to evaluate its efficacy.

Background parenchymal enhancement on contrast-enhanced spectral mammography does not represent an influencing factor for breast cancer: A preliminary study

To compare the relationship between background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM), mammographic breast density (MBD), age, in the group with benign vs malignant breast lesions.Four hundred thirty three non-high-risk patients from January 2018 to May 2019 were retrospectively analyzed. Patients were assigned into 4 groups: premenopausal benign lesions, premenopausal malignant lesions, postmenopausal benign lesions, and postmenopausal malignant lesions. The differences in CESM BPE and MBD between premenopausal benign lesions and premenopausal malignant lesions, between postmenopausal benign lesions and postmenopausal malignant lesions, between premenopausal and postmenopausal benign lesions, and between premenopausal and postmenopausal malignant lesions were evaluated. Pearson Chi-Squared test was used to analyze the differences between the above groups. Spearman rank correlation analysis was used to evaluate the correlations between BPE, MBD, and age. Multiple logistic regression was used to analyze the influencing factors of breast cancer. P < .05 was considered statistically significant.There was no significant difference in CESM BPE or MBD of benign and malignant lesions regardless of premenopausal or postmenopausal status, but there was a significant difference in CESM BPE and MBD of premenopausal and postmenopausal patients regardless of the presence of benign or malignant lesions. The intensity of CESM BPE was positively correlated with MBD, and the intensity of CESM BPE and MBD were negatively correlated with age. Multiple logistic regression analysis showed that age was an influencing factor for breast cancer in both premenopausal and postmenopausal patients.For non-high-risk women, CESM BPE and MBD were not correlated with benign or malignant breast lesions, and age was an influencing factor for breast cancer.

Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approach

Marked enhancement of the fibroglandular tissue on contrast-enhanced breast magnetic resonance imaging (MRI) may affect lesion detection and classification and is suggested to be associated with higher risk of developing breast cancer. The background parenchymal enhancement (BPE) is qualitatively classified according to the BI-RADS atlas into the categories "minimal," "mild," "moderate," and "marked." The purpose of this study was to train a deep convolutional neural network (dCNN) for standardized and automatic classification of BPE categories.This IRB-approved retrospective study included 11,769 single MR images from 149 patients. The MR images were derived from the subtraction between the first post-contrast volume and the native T1-weighted images. A hierarchic approach was implemented relying on 2 dCNN models for detection of MR-slices imaging breast tissue and for BPE classification, respectively. Data annotation was performed by 2 board-certified radiologists. The consensus of the 2 radiologists was chosen as reference for BPE classification. The clinical performances of the single readers and of the dCNN were statistically compared using the quadratic Cohen's kappa.Slices depicting the breast were classified with training, validation, and real-world (test) accuracies of 98%, 96%, and 97%, respectively. Over the 4 classes, the BPE classification was reached with mean accuracies of 74% for training, 75% for the validation, and 75% for the real word dataset. As compared to the reference, the inter-reader reliabilities for the radiologists were 0.780 (reader 1) and 0.679 (reader 2). On the other hand, the reliability for the dCNN model was 0.815.Automatic classification of BPE can be performed with high accuracy and support the standardization of tissue classification in MRI.