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Automatic diagnosis systems capable of handling multiple pathologies are essential in clinical practice. This study focuses on enhancing precise lesion localization, classification and delineation in transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence. Despite deep learning models success, medical applications face challenges like small and limited datasets and poor image characterization, including the absence lack of color/texture modeling. To address these issues, three solutions are proposed: (1) an improved texture-constrained version of the pix2pixHD cGAN for data augmentation, addressing the tradeoff of generating high-quality images with enough stochasticity using the Fréchet Inception Distance (FID) measure. (2) Introducing the Multiple Mask and Boundary Scoring R-CNN (MM&BS R-CNN), a new mask sub-net scheme where multiple masks are generated from the different levels of the mask sub-net pipeline, improving segmentation accuracy by including a new scoring module to refine object boundaries. (3) A novel accelerated training strategy based on the SGD optimizer with the second momentum. Experimental results show significant mAP improvements: the data generation scheme improves by more than 12 %; MM&BS R-CNN proposed architecture is responsible for an improvement of about 1.25 %, and the training algorithm based on the second-order momentum increases mAP by 2–3 %. The simultaneous use of all three proposals improved the state-of-the-art mAP by 17.44 %. • Improved instance segmentation network for bladder cancer addressing multi-pathology classification and the critical aspect of bad cancer resection that leads to cancer recurrence. • Data augmentation using an texture-constrained version of the pix2pixHD cGAN, generating high-quality images with enough stochasticity to promote generalization capability in limited datasets. • Increase of about 2-3% in mAP by training with the proposed second-order momentum optimizer. • Increase of 17.44% in mAP compared with BMask R-CNN. [ABSTRACT FROM AUTHOR]

Background: Current surgical and ablative treatment options for prostate cancer (PCa) may result in a high incidence of (temporary) incontinence, erectile dysfunction and/or bowel damage. These side effects are due to procedure related effects on adjacent structures including blood vessels, bowel, urethra and/or neurovascular bundle. Ablation with irreversible electroporation (IRE) has shown to be effective and safe in destroying PCa cells and also has the potential advantage of sparing surrounding tissue and vital structures, resulting in less impaired functional outcomes and maintaining men's quality of life.Methods/design: In this randomized controlled trial (RCT) on IRE in localized PCa, 200 patients with organ-confined, unilateral (T1c-T2b) low- to intermediate-risk PCa (Gleason sum score 6 and 7) on transperineal template-mapping biopsies (TTMB) will be included. Patients will be randomized into focal or extended ablation of cancer foci with IRE. Oncological efficacy will be determined by multiparametric Magnetic Resonance Imaging, Contrast-Enhanced Ultrasound imaging if available, TTMP and Prostate Specific Antigen (PSA) follow-up. Patients will be evaluated up to 5 years on functional outcomes and quality of life with the use of standardized questionnaires.Discussion: There is critical need of larger, standardized RCTs evaluating long-term oncological and functional outcomes before introducing IRE and other focal therapy modalities as an accepted and safe therapeutic option for PCa. This RCT will provide important short- and long-term data and elucidates the differences between focal or extended ablation of localized, unilateral low- to intermediate-risk PCa with IRE.Trial Registration: Clinicaltrials.gov database registration number NCT01835977. The Dutch Central Committee on Research Involving Human Subjects registration number NL50791.018.14. [ABSTRACT FROM AUTHOR]

This paper confronts two approaches to classify bladder lesions shown in white light cystoscopy images when using small datasets: the classical one, where handcrafted-based features feed pattern recognition systems and the modern deep learning-based (DL) approach. In between, there are alternative DL models that had not received wide attention from the scientific community, even though they can be more appropriate for small datasets such as the human brain motivated capsule neural networks (CapsNets). However, CapsNets have not yet matured hence presenting lower performances than the most classic DL models. These models require higher computational resources, more computational skills from the physician and are more prone to overfitting, making them sometimes prohibitive in the routine of clinical practice. This paper shows that carefully handcrafted features used with more robust models can reach similar performances to the conventional DL-based models and deep CapsNets, making them more useful for clinical applications. Concerning feature extraction, it is proposed a new feature fusion approach for Ta and T1 bladder tumor detection by using decision fusion from multiple classifiers in a scheme known as stacking of classifiers. Three Neural Networks perform classification on three different feature sets, namely: Covariance of Color Histogram of Oriented Gradients, proposed in the ambit of this paper; Local Binary Patterns and Wavelet Coefficients taken from lower scales. Data diversity is ensured by a fourth Neural Network, which is used for decision fusion by combining the outputs of the ensemble elements to produce the classifier output. Both Feed Forward Neural Networks and Radial Basis Functions are used in the experiments. Contrarily, DL-based models extract automatically the best features at the cost of requiring huge amounts of training data, which in turn can be alleviated by using the Transfer Learning (TL) strategy. In this paper VGG16 and ResNet-34 pretrained in ImageNet were used for TL, slightly outperforming the proposed ensemble. CapsNets may overcome CNNs given their ability to deal with objects rotational invariance and spatial relationships. Therefore, they can be trained from scratch in applications using small amounts of data, which was beneficial for the current case, improving accuracy from 94.6% to 96.9%. [ABSTRACT FROM AUTHOR]

Contemporary minimally invasive surgical (MIS) treatment options of patients with male Lower Urinary Tract Symptoms (LUTS) in men with prostate glands >80 mL include Holmium Laser Enucleation Prostate (HoLEP), Thulium laser VapoEnucleation Prostate (ThuVEP), and Laparoscopic (LSP) or Robotic-Assisted Simple Prostatectomy (RASP). Implementing new laser technologies is costly, and the steep learning curve of these laser techniques limit their wide range use. This promoted the use of LSP and RASP in centers with readily established laparoscopy or robotic surgery programs. The aim of this study is to review case and comparative series of RASP. We systematically reviewed published data from 2008 to 2020 on RASP and have identified 26 non-comparative and 9 comparative case series. RASP has longer operation time but less time spent in hospital and less blood loss. The outcomes of improvements in symptom score, post-voiding residual urine (PVR), postoperative PSA decline, complications, and cost are similar when compared to open and laser enucleation techniques. These outcomes position RASP as a viable MIS treatment option for patients with male LUTS needing surgical treatment for enlarged prostates. Nevertheless, prospective, randomized controlled trials (RCTs) with multicenter and large sample size are needed to confirm the findings of this systematic review. [ABSTRACT FROM AUTHOR]