Is tadalafil associated with decreased risk of major adverse cardiac events or venous thromboembolism in men with lower urinary tract symptoms?

PURPOSE: To evaluate the association of tadalafil, a phosphodiesterase-5 inhibitor (PDE5I), with major adverse cardiac events (MACE) or venous thromboembolism (VTE) in men with lower urinary tract symptoms (LUTS). METHODS: Data was obtained from the TriNetX Research Network, ICD-10 codes were used to identify men with LUTS, MACE, and VTE. In addition, demographic characteristics and use of tadalafil or alpha-blocker was evaluated. Then, unbalanced and balanced association analyses was performed to assess the relation between tadalafil and/or alpha-blocker use with MACE/VTE. RESULTS: After participant selection, analysis included 821,592 men that did not use an alpha blocker or tadalafil, 5,004 men that used tadalafil but no alpha blocker, 327,482 men that used an alpha blocker but no tadalafil, and 6,603 men that used both an alpha blocker and tadalafil. On balanced analysis, tadalafil was independently associated with a decreased risk of MACE/VTE within a 3-year time period (OR = 0.59, 95%CI 0.49-0.70, p < 0.0001). Among men with a history of alpha blocker use, tadalafil use was also independently associated with a decreased risk of MACE or VTE, both before and after controlling for potentially confounding variables (OR = 0.57, 95%CI: 0.50-0.66; p < 0.0001). CONCLUSIONS: In our study, tadalafil was associated with a decreased risk of MACE/VTE in men with LUTS with and without a history of alpha blocker use. It is time to perform further long-term prospective randomized studies to further analyze the cardiovascular effects of PDE5Is as combination treatment with alpha blockers in the management of LUTS.

Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images.

The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data for a particular medical condition. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition. However, despite some success, GAN model usage remains largely minimal when depicting the heterogeneity of a disease such as prostate cancer. Previous studies from our group members have focused on automating the quantitative multi-parametric magnetic resonance imaging (mpMRI) using habitat risk scoring (HRS) maps on the prostate cancer patients in the BLaStM trial. In the current study, we aimed to use the images from the BLaStM trial and other sources to train the GAN models, generate synthetic images, and validate their quality. In this context, we used T2-weighted prostate MRI images as training data for Single Natural Image GANs (SinGANs) to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degrees of experience (more than ten years, one year, or no experience) to work with MRI images. Results showed that the most experienced participating group correctly identified conventional vs. synthetic images with 67% accuracy, the group with one year of experience correctly identified the images with 58% accuracy, and the group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional. Interestingly, in a blinded quality assessment, a board-certified radiologist did not significantly differentiate between conventional and synthetic images in the context of the mean quality of synthetic and conventional images. Furthermore, to validate the usability of the generated synthetic images from prostate cancer MRIs, we subjected these to anomaly detection along with the original images. Importantly, the success rate of anomaly detection for quality control-approved synthetic data in phase one corresponded to that of the conventional images. In sum, this study shows promise that high-quality synthetic images from MRIs can be generated using GANs. Such an AI model may contribute significantly to various clinical applications which involve supervised machine-learning approaches.