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Objective: To compare the safety and efficacy of the en bloc technique with the standard 2-lobe technique for holmium laser enucleation of the prostate (HoLEP). Methods: This prospective study included patients with benign prostatic hyperplasia (BPH) who underwent HoLEP from September 2020 to March 2022, by en bloc or 2-lobe technique. Patient demographics, prostate volume, enucleation, morcellation and operative time, and incidence of postoperative incontinence were compared between the 2 groups. Results: We included 64 patients (30 en bloc and 34 2-lobe techniques) who underwent HoLEP in this study. The mean age, prostate volume, creatinine, and PSA of patients were comparable in both groups [(68.53 vs. 67.55 years; P = .62), (79.43 vs. 79.88 g, P = .92), (1.08 mg/dL vs. 1.20 mg/dL, P = .35), (3.78 vs. 4.63 ng/mL; P = .376), respectively]. The enucleation time was significantly shorter in the en bloc group than in the 2-lobe group (54.2 vs. 61.67; P = .03). Additionally, the mean operative time was also comparatively shorter in the en bloc group than the 2-lobe group (72.36 vs. 80.50; P = .057). The improvement in the quality-of-life (QoL) score was significantly better with en bloc than the 2-lobe group (3.80 vs. 2.11; P = .01). There was a significant difference in stress urinary incontinence on days 1, 7, and 30 (P < .001) with en bloc compared to the two-lobe technique. Conclusion: Although the outcomes of en bloc and 2-lobe endoscopic enucleation of prostate techniques were comparable, the en bloc technique seems to be a better option in most patients undergoing HoLEP due to less enucleation and operative time and lowered stress urinary incontinence incidence. [ABSTRACT FROM AUTHOR]

This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer diseases, primarily the brain tumor, have been exponentially raised which has alarmed researchers from academia and industry. Nowadays, researchers need to attain a more effective, accurate, and trustworthy brain tumor tissue detection and classification approach. Different from traditional machine learning methods that are just targeting to enhance classification efficiency, this work highlights the process to extract several deep features to diagnose brain tumor effectively. This paper explains the modeling of a novel technique by integrating the modified ResNet50 with the Enhanced Watershed Segmentation (EWS) algorithm for brain tumor classification and deep feature extraction. The proposed model uses the ResNet50 model with a modified layer architecture including five convolutional layers and three fully connected layers. The proposed method can retain the optimal computational efficiency with high-dimensional deep features. This work obtains a comprised feature set by retrieving the diverse deep features from the ResNet50 deep learning model and feeds them as input to the classifier. The good performing capability of the proposed model is achieved by using hybrid features of ResNet50. The brain tumor tissue images were extracted by the suggested hybrid deep feature-based modified ResNet50 model and the EWS-based modified ResNet50 model with a high classification accuracy of 92% and 90%, respectively. [ABSTRACT FROM AUTHOR]