Clinical and Urodynamic Predictors for Failure of Medical Management with Alpha Blockers in PBNO: A Retrospective Cohort Analysis.

OBJECTIVE: To evaluate the clinical and urodynamic variables that may predict the failure of alpha-blockers in primary bladder neck obstruction (PBNO) patients. Alpha-blockers are useful as a treatment option in patients with PBNO. Nonresponders need to undergo bladder neck incision (BNI). Little is known about the predictive factors determining the success of treatment. MATERIALS AND METHODS: This was a retrospective study, spanning over a period of 8 years. PBNO was diagnosed in the presence of a bladder outlet obstruction index (BOOI) >40 with video-urodynamic evidence of obstruction at the bladder neck. The patients were initially managed with alpha-blockers (alfuzosin and tamsulosin) for 3-6 months, and BNI contemplated when pharmacotherapy failed. The patients with upper tract changes managed with upfront BNI or clean intermittent catheterization were excluded. The data for the international prostate symptom score (IPSS), uroflowmetry, urodynamic studies, and ultrasonography of pre and post-treatment periods were reviewed. Treatment outcomes were defined as complete response (>50% improvement in Qmax and IPSS score) and partial response (30%-50% improvement in Qmax and IPSS score) at 3 or 6 months. RESULTS: Ninety-nine patients were analyzed. 21 patients underwent BNI for the failure of medical management and 31 for recurrence of symptoms at a mean follow-up of 18.8 ± 3.5 months (12-70 months). Independent predictors of failure of pharmacotherapy with alpha-blockers were age (P = .021), Pdet@Qmax (P = .015), and BOOI (P = .019). CONCLUSION: Alpha-blockers are more likely to fail in PBNO in younger patients generating higher voiding pressures and BOOI > 60.

Is systematic prostate biopsy an overkill in metastatic prostate carcinoma ? A prospective validation.

PURPOSE: To assess the efficacy of 2-core prostate biopsy in advanced prostate cancer patients. This included a retrospective analysis of 12-core prostate biopsies and a prospective validation that a reduced number of cores are sufficient for histopathological diagnosis. METHODS: The first phase analyzed retrospective data from 12-core prostate biopsies between January 2013 and 2018. In the second phase, from January 2018 to January 2022, in a prospective setting, patients with PSA > 75 ng/dl underwent bone scans first. Those with positive bone scans underwent a 2-core biopsy. Cancer detection rate and complications were analyzed to validate the findings of the first phase. RESULTS: In the retrospective analysis, the number of positive cores in metastatic disease was 12 in 93 (73.8%), 11 in 14 (11.1%), and 10 in 7 (5.6%) patients. Using probability analysis, 94% of patients with metastasis could be detected with a single core and 97.8% with a 2-core biopsy. In the prospective analysis, 52 patients with PSA > 75 were enrolled. 3/52 (5.7%) patients had a negative bone scan. 49 were assigned for 2-core biopsy, out of which 48 (97.9%) had a positive result. One patient underwent a repeat 12-core biopsy. The prospective cohort's complications (p = 0.003) and pain score (p = 0.03) were lower compared to patients who underwent standard 12-core biopsies during phase one of the study period. CONCLUSION: A 2-core biopsy is adequate in almost all patients with metastatic prostate cancer with PSA > 75, and this avoids excess complications and morbidity associated with a systematic 12-core prostate biopsy.

Spatial feature and resolution maximization GAN for bone suppression in chest radiographs.

BACKGROUND AND OBJECTIVE: Chest radiographs (CXR) are in great demand for visualizing the pathology of the lungs. However, the appearance of bones in the lung region hinders the localization of any lesion or nodule present in the CXR. Thus, bone suppression becomes an important task for the effective screening of lung diseases. Simultaneously, it is equally important to preserve spatial information and image quality because they provide crucial insights on the size and area of infection, color accuracy, structural quality, etc. Many researchers considered bone suppression as an image denoising problem and proposed conditional Generative Adversarial Network-based (cGAN) models for generating bone suppressed images from CXRs. These works do not focus on the retention of spatial features and image quality. The authors of this manuscript developed the Spatial Feature and Resolution Maximization (SFRM) GAN to efficiently minimize the visibility of bones in CXRs while ensuring maximum retention of critical information. METHOD: This task is achieved by modifying the architectures of the discriminator and generator of the pix2pix model. The discriminator is combined with the Wasserstein GAN with Gradient Penalty to increase its performance and training stability. For the generator, a combination of different task-specific loss functions, viz., L1, Perceptual, and Sobel loss are employed to capture the intrinsic information in the image. RESULT: The proposed model reported as measures of performance a mean PSNR of 43.588, mean NMSE of 0.00025, mean SSIM of 0.989, and mean Entropy of 0.454 bits/pixel on a test size of 100 images. Further, the combination of δ=104, α=1, ß=10, and γ=10 are the hyperparameters that provided the best trade-off between image denoising and quality retention. CONCLUSION: The degree of bone suppression and spatial information preservation can be improved by adding the Sobel and Perceptual loss respectively. SFRM-GAN not only suppresses bones but also retains the image quality and intrinsic information. Based on the results of student's t-test it is concluded that SFRM-GAN yields statistically significant results at a 0.95 level of confidence and shows its supremacy over the state-of-the-art models. Thus, it may be used for denoising and preprocessing of images.

Thrombus Masquerading a Double J Ureteric Stent.

Renal cell carcinoma (RCC) is the most common renal parenchymal malignancy found in adults. When these tumors are located centrally in the kidney and do not enhance well on contrast imaging, they may be mistaken to be urothelial in origin, and the diagnosis is established on histopathology. We present an interesting case of RCC with an atypical vermiform thrombus projecting into the urinary bladder.

A deep learning model for mass screening of COVID-19.

The objective of this research is to develop a convolutional neural network model 'COVID-Screen-Net' for multi-class classification of chest X-ray images into three classes viz. COVID-19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X-ray images and accurately identifies the features responsible for distinguishing the X-ray images of different classes. It plots these features on the GradCam. The authors optimized the number of convolution and activation layers according to the size of the dataset. They also fine-tuned the hyperparameters to minimize the computation time and to enhance the efficiency of the model. The performance of the model has been evaluated on the anonymous chest X-ray images collected from hospitals and the dataset available on the web. The model attains an average accuracy of 97.71% and a maximum recall of 100%. The comparative analysis shows that the 'COVID-Screen-Net' outperforms the existing systems for screening of COVID-19. The effectiveness of the model is validated by the radiology experts on the real-time dataset. Therefore, it may prove a useful tool for quick and low-cost mass screening of patients of COVID-19. This tool may reduce the burden on health experts in the present situation of the Global Pandemic. The copyright of this tool is registered in the names of authors under the laws of Intellectual Property Rights in India with the registration number 'SW-13625/2020'.