Albuminuria-lowering effect of dapagliflozin, exenatide, and their combination in patients with type 2 diabetes: A randomized cross-over clinical study.

AIM: To evaluate the albuminuria-lowering effect of dapagliflozin, exenatide, and the combination of dapagliflozin and exenatide in patients with type 2 diabetes and microalbuminuria or macroalbuminuria. METHODS: Participants with type 2 diabetes, an estimated glomerular filtration rate (eGFR) of more than 30 ml/min/1.73m2 and an urinary albumin: creatinine ratio (UACR) of more than 3.5 mg/mmol and 100 mg/mmol or less completed three 6-week treatment periods, during which dapagliflozin 10 mg/d, exenatide 2 mg/wk and both drugs combined were given in random order. The primary outcome was the percentage change in UACR. Secondary outcomes included blood pressure, HbA1c, body weight, extracellular volume, fractional lithium excretion and renal haemodynamic variables as determined by magnetic resonance imaging. RESULTS: We enrolled 20 patients, who completed 53 treatment periods in total. Mean percentage change in UACR from baseline was -21.9% (95% CI: -34.8% to -6.4%) during dapagliflozin versus -7.7% (95% CI: -23.5% to 11.2%) during exenatide and -26.0% (95% CI: -38.4% to -11.0%) during dapagliflozin-exenatide treatment. No correlation was observed in albuminuria responses between the different treatments. Numerically greater reductions in systolic blood pressure, body weight and eGFR were observed during dapagliflozin-exenatide treatment compared with dapagliflozin or exenatide alone. Renal blood flow and effective renal plasma flow (ERPF) did not significantly change with either treatment regimen. However, all but four and two patients in the dapagliflozin and dapagliflozin-exenatide groups, respectively, showed reductions in ERPF. The filtration fraction did not change during treatment with dapagliflozin or exenatide, and decreased during dapagliflozin-exenatide treatment (-1.6% [95% CI: -3.2% to -0.01%]; P = .048). CONCLUSIONS: In participants with type 2 diabetes and albuminuria, treatment with dapagliflozin, exenatide and dapagliflozin-exenatide reduced albuminuria, with a numerically larger reduction in the combined dapagliflozin-exenatide treatment group.

A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.

OBJECTIVES: To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI). MATERIALS AND METHODS: This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion). The DLM auto-fixed VOI method uses a spherical VOI (with its center at the location of the lowest apparent diffusion coefficient of the prostate lesion as indicated with a single mouse click) from which non-prostate voxels are removed using a deep learning-based prostate segmentation algorithm. Thirteen different DLM auto-fixed VOI diameters (ranging from 6 to 30 mm) were explored. Extracted radiomics data were split into training and test sets (4:1 ratio). Performance was assessed with receiver operating characteristic (ROC) analysis. RESULTS: In the test set, the area under the ROC curve (AUCs) of the DLM auto-fixed VOI method with a VOI diameter of 18 mm (0.76 [95% CI: 0.66-0.85]) was significantly higher (p = 0.0198) than that of the manual segmentation method (0.62 [95% CI: 0.52-0.73]). CONCLUSIONS: A DLM auto-fixed VOI segmentation can provide a potentially more accurate radiomics diagnosis of CS PCa than expert manual segmentation while also reducing expert time investment by more than 97%. KEY POINTS: • Compared to traditional expert-based segmentation, a deep learning mask (DLM) auto-fixed VOI placement is more accurate at detecting CS PCa. • Compared to traditional expert-based segmentation, a DLM auto-fixed VOI placement is faster and can result in a 97% time reduction. • Applying deep learning to an auto-fixed VOI radiomics approach can be valuable.

Multimodal AI Combining Clinical and Imaging Inputs Improves Prostate Cancer Detection.

OBJECTIVES: Deep learning (DL) studies for the detection of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) often overlook potentially relevant clinical parameters such as prostate-specific antigen, prostate volume, and age. This study explored the integration of clinical parameters and MRI-based DL to enhance diagnostic accuracy for csPCa on MRI. MATERIALS AND METHODS: We retrospectively analyzed 932 biparametric prostate MRI examinations performed for suspected csPCa (ISUP ≥2) at 2 institutions. Each MRI scan was automatically analyzed by a previously developed DL model to detect and segment csPCa lesions. Three sets of features were extracted: DL lesion suspicion levels, clinical parameters (prostate-specific antigen, prostate volume, age), and MRI-based lesion volumes for all DL-detected lesions. Six multimodal artificial intelligence (AI) classifiers were trained for each combination of feature sets, employing both early (feature-level) and late (decision-level) information fusion methods. The diagnostic performance of each model was tested internally on 20% of center 1 data and externally on center 2 data (n = 529). Receiver operating characteristic comparisons determined the optimal feature combination and information fusion method and assessed the benefit of multimodal versus unimodal analysis. The optimal model performance was compared with a radiologist using PI-RADS. RESULTS: Internally, the multimodal AI integrating DL suspicion levels with clinical features via early fusion achieved the highest performance. Externally, it surpassed baselines using clinical parameters (0.77 vs 0.67 area under the curve [AUC], P < 0.001) and DL suspicion levels alone (AUC: 0.77 vs 0.70, P = 0.006). Early fusion outperformed late fusion in external data (0.77 vs 0.73 AUC, P = 0.005). No significant performance gaps were observed between multimodal AI and radiologist assessments (internal: 0.87 vs 0.88 AUC; external: 0.77 vs 0.75 AUC, both P > 0.05). CONCLUSIONS: Multimodal AI (combining DL suspicion levels and clinical parameters) outperforms clinical and MRI-only AI for csPCa detection. Early information fusion enhanced AI robustness in our multicenter setting. Incorporating lesion volumes did not enhance diagnostic efficacy.

Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer.

OBJECTIVES: To investigate a previously developed radiomics-based biparametric magnetic resonance imaging (bpMRI) approach for discrimination of clinically significant peripheral zone prostate cancer (PZ csPCa) using multi-center, multi-vendor (McMv) and single-center, single-vendor (ScSv) datasets. METHODS: This study's starting point was a previously developed ScSv algorithm for PZ csPCa whose performance was demonstrated in a single-center dataset. A McMv dataset was collected, and 262 PZ PCa lesions (9 centers, 2 vendors) were selected to identically develop a multi-center algorithm. The single-center algorithm was then applied to the multi-center dataset (single-multi-validation), and the McMv algorithm was applied to both the multi-center dataset (multi-multi-validation) and the previously used single-center dataset (multi-single-validation). The areas under the curve (AUCs) of the validations were compared using bootstrapping. RESULTS: Previously the single-single validation achieved an AUC of 0.82 (95% CI 0.71-0.92), a significant performance reduction of 27.2% compared to the single-multi-validation AUC of 0.59 (95% CI 0.51-0.68). The new multi-center model achieved a multi-multi-validation AUC of 0.75 (95% CI 0.64-0.84). Compared to the multi-single-validation AUC of 0.66 (95% CI 0.56-0.75), the performance did not decrease significantly (p value: 0.114). Bootstrapped comparison showed similar single-center performances and a significantly different multi-center performance (p values: 0.03, 0.012). CONCLUSIONS: A single-center trained radiomics-based bpMRI model does not generalize to multi-center data. Multi-center trained radiomics-based bpMRI models do generalize, have equal single-center performance and perform better on multi-center data.

The neutrophil: the unnoticed threat in xenotransplantation?

BACKGROUND: Xenotransplantation offers one way to circumvent the widening gap between the demand for and supply of human organs for transplantation, and the pig is widely regarded as the donor animal most likely to prove appropriate. Most attention has focused on the adaptive immune response to xenogeneic tissue. However, there is optimism that it may soon be possible to overcome that hurdle. In this paper, we consider the possibility of the direct recognition of xenogeneic tissue by neutrophils. METHODS: We studied in vitro the interaction of human neutrophils with cultured porcine endothelial cells in assays of adhesion (both static and flow), activation on the basis of chemiluminescence, and diapedesis and chemotaxis using split-well chambers. RESULTS: Human neutrophils showed increased adhesiveness to porcine endothelium in both static and flow adhesion systems. While this did not activate the neutrophils at rest, in the presence of suboptimal concentrations of a parallel stimulus, phorbol myristate acetate, the interaction of human neutrophils with porcine endothelium caused a much greater respiratory burst than their interaction with controls. In addition, they showed greater diapedesis through porcine endothelium. Of greatest interest is the observation that porcine endothelium secretes a molecule that is chemotactic for human neutrophils. CONCLUSIONS: On the basis of these observations, we should consider the potential for neutrophil-mediated low-grade damage to xenografts emerging as a significant problem when others have been circumvented.